
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


train = pd.read_csv('./train.csv').drop_duplicates()
test = pd.read_csv('./test.csv')
test_ids = pd.read_csv('./test_ids.csv')


test


train.head(3).T


test.head(3).T


train['time_stamp'] = pd.to_datetime(train['time_stamp'])

train.loc[:,'month'] = train.loc[:,'time_stamp'].dt.strftime('%m')
train.loc[:,'dayOfMonth'] = train.loc[:,'time_stamp'].dt.strftime('%d')
train.loc[:,'dayofWeek'] = train.loc[:,'time_stamp'].dt.day_name()
train.loc[:,'hour'] = train.loc[:,'time_stamp'].dt.strftime('%H')
train.loc[:,'date'] = train.loc[:,'time_stamp'].dt.strftime('%Y-%m-%d')
train['hour'] = (train['hour']).astype(int)
train['dayOfMonth'] = (train['dayOfMonth']).astype(int)
train['month'] = (train['month']).astype(int)

train['date'] = train['time_stamp'].dt.date
train['date'] = pd.to_datetime(train['date'])


test['time_stamp'] = pd.to_datetime(test['time_stamp'])

test.loc[:,'month'] = test.loc[:,'time_stamp'].dt.strftime('%m')
test.loc[:,'dayOfMonth'] = test.loc[:,'time_stamp'].dt.strftime('%d')
test.loc[:,'dayofWeek'] = test.loc[:,'time_stamp'].dt.day_name()
test.loc[:,'hour'] = test.loc[:,'time_stamp'].dt.strftime('%H')
test.loc[:,'date'] = test.loc[:,'time_stamp'].dt.strftime('%Y-%m-%d')
test['hour'] = (test['hour']).astype(int)
test['dayOfMonth'] = (test['dayOfMonth']).astype(int)
test['month'] = (test['month']).astype(int)

test['date'] = test['time_stamp'].dt.date
test['date'] = pd.to_datetime(test['date'])


train.user_action.value_counts(dropna=False).head(15)

visit       2827388
search      1932734
favorite     396576
basket       265274
order         71296
Name: user_action, dtype: int64


test.user_action.value_counts().head(15)

visit       1210149
search       801985
favorite     178786
basket       106744
order         27150
Name: user_action, dtype: int64


test.Level1_Category_Name.value_counts().head(15)

Giyim                       1036942
Ev & Mobilya                 376747
Ayakkabı                     239799
Aksesuar                     155003
Kozmetik & Kişisel Bakım     125843
Süpermarket                  106172
Elektronik                   100130
Yaşam                         99270
Anne & Bebek & Çocuk          42389
Spor & Outdoor                40342
Name: Level1_Category_Name, dtype: int64


train.product_gender.value_counts(dropna=False).head(15)

Kadın     2841958
Unisex    1459890
NaN        621972
Erkek      569448
Name: product_gender, dtype: int64


train[['unique_id', 'gender']].drop_duplicates().gender.value_counts(dropna=False).head(15)

F    3679
M    1939
Name: gender, dtype: int64


train.brand_name.value_counts(dropna=False)

TRENDYOLMİLLA     599699
Koton             184011
LC Waikiki        169010
DeFacto            76596
Happiness İst.     75433
                   ...  
TÜRELİ GİYİM           1
Isımat                 1
BLACK CONCEPT          1
miss slim easy         1
B'LOCK                 1
Name: brand_name, Length: 33333, dtype: int64


train.Level1_Category_Name.value_counts(dropna=False)

Giyim                       2450653
Ev & Mobilya                 921736
Ayakkabı                     508360
Aksesuar                     363618
Kozmetik & Kişisel Bakım     323510
Elektronik                   247494
Süpermarket                  246607
Yaşam                        246522
Spor & Outdoor                96296
Anne & Bebek & Çocuk          82834
NaN                            5638
Name: Level1_Category_Name, dtype: int64


train.businessunit.value_counts(dropna=False)

Kadın A                               566603
Kadın B                               553513
PL Woman                              533900
Sofra & Mutfak                        287820
Branded Shoes B                       244010
Kids & Baby Fashion                   233735
Ev Dekorasyon                         154555
Ev Tekstil                            143833
Sportswear                            129567
İç Giyim                              127875
Branded Shoes A                       113900
Makyaj                                112576
Ev Giyim                               95838
Takı                                   93302
Cilt Bakım                             89271
Erkek B                                86092
Cep Telefonu & Aksesuarları            80879
Sağlık                                 79370
Salon &Mutfak Mobilya                  76636
Çanta & Valiz                          75263
Hobi                                   69795
GAS Denim                              65956
Elektrikli Ev Aletleri                 64387
Erkek A                                63916
Oyuncak                                61659
Halı/Kilim                             61041
Gıda ve İçecek                         59637
Kişisel Bakım                          59443
Kırtasiye & Ofis                       53441
Banyo                                  52123
PL Homewear                            51932
Bahçe & Yapı Market & Hırdavat         48586
Ev Gereçleri                           48493
Konservatif                            46215
PL Erkek                               46002
Books                                  41663
Aydınlatma                             40573
GAS Casual                             40271
Ev Bakım ve Temizlik                   38279
Yatak Oda Mobilyası                    35183
Bilgisayar & Tablet & Aksesuarları     33666
Diğer Aksesuarlar                      33614
Büyük Beden                            31864
Ofis Mobilyası                         31642
Saat B                                 30857
Çocuk Gereç                            28648
Saç Bakım                              26712
Oto Aksesuar                           22150
Beyaz Eşya                             20860
Parfüm                                 20505
Fantezi Giyim                          20323
Sport Equipment                        20303
Petshop                                19655
Anne & Bebek Bakım                     19122
Kişisel Bakım Aletleri                 18629
TV & Görüntü Ses Sistemleri            17056
Outdoor                                16967
Luxury                                 16059
Mücevher                               13948
Bebek Bezi & Mendil                    12087
Bahçe Mobilyası                        11941
Saat A                                 11457
Outdoor Equipment                      10495
Vücut Bakım                            10409
PL Party & Wedding                     10011
Gözlük B                                9613
Gözlük A                                8605
Designer                                7924
Mayo                                    6049
PL Beach                                5681
NaN                                     5638
Foto & Kamera                           4438
Digital Goods                           3557
Oyun & Oyun Konsolları                  3310
PL Ayakkabı                             1404
PL Designer                              432
Çocuk                                    135
Aksesuar                                 119
Mobilya                                   79
Elektronik                                74
GAS                                       50
Lüks & Tasarım                            27
Kitap & Kırtasiye & Yaşam                 13
Ev                                         7
Name: businessunit, dtype: int64


train['size'] = train['time_stamp'].groupby(train['time_stamp']).transform('count')


plt.figure(figsize=(16,4))
plt.plot(train.groupby('time_stamp').size())

[<matplotlib.lines.Line2D at 0x7faae0346610>]


plt.figure(figsize=(16,4))
plt.plot(test.groupby('time_stamp').size())

[<matplotlib.lines.Line2D at 0x7facb6f26a60>]


# 3991 Male, 7396 Female, 4259 Male ,7613 Male galerici, 6714 (Kategoriden bir anne ? ), 1228 Male (Yalan)
# 6400 Male bilmek imkansız,3922 Zorrr, 6225 imkansız, 7471 (bakılan distinct kategori sayısı ? ), 4108
# ürünle ilgili her şey na 
pd.set_option('display.max_rows', None)
pd.set_option('display.max_column', None)

train[train['unique_id']==3991].sort_values('date')
#pd.reset_option('display.max_rows')


# Pass the axes into seaborn
sns.countplot(x="gender", hue = "product_gender",data=train[train["businessunit"]=='Mücevher']);


fig, axes = plt.subplots(3, 2, figsize=(16, 8))

# Use the axes for plotting
# axes[0,0].plot(years, apples, 's-b')
axes[0,0].set_title('User Action per Gender in Train set')
sns.countplot(x="user_action", hue="gender", data=train, ax=axes[0,0])


# Pass the axes into seaborn
axes[0,1].set_title('Gender Distribution in Train set')
sns.countplot(x="gender", data=train, ax=axes[0,1]);

# Pass the axes into seaborn
axes[1,0].set_title('User Action Distribution in Train Set')
sns.countplot(x="user_action", data=train, ax=axes[1,0]);

# Pass the axes into seaborn
axes[1,1].set_title('User Action Distribution in Test Set')
sns.countplot(x="user_action", data=test, ax=axes[1,1])


# Pass the axes into seaborn
axes[2,0].set_title('Product Gender Distribution in Train set')
sns.countplot(x="product_gender", data=train, ax=axes[2,0]);

# Pass the axes into seaborn
axes[2,1].set_title('Product Gender Distribution in test set')
sns.countplot(x="product_gender", data=test, ax=axes[2,1]);

plt.tight_layout(pad=2);

<AxesSubplot:title={'center':'User Action Distribution in Test Set'}, xlabel='user_action', ylabel='count'>


ax = sns.countplot(x="dayofWeek", data=train, hue = 'user_action', order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
plt.tight_layout(pad=2);


ax = sns.countplot(x="hour", data=train, order = range(0,24))


g = sns.catplot(x="hour", hue="type", col="gender",
                data=train, kind="count",
                height=10, aspect=1, order = range(0,24) );


g = sns.catplot(x="dayofWeek", hue="type", col="gender",
                data=train, kind="count",
                height=10, aspect=1, order =  ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] );


train['date'] = train['time_stamp'].dt.date
train['date'] = pd.to_datetime(train['date'])


train.dtypes

time_stamp              datetime64[ns, UTC]
contentid                           float64
user_action                          object
sellingprice                        float64
product_name                         object
brand_id                            float64
brand_name                           object
businessunit                         object
product_gender                       object
category_id                         float64
Level1_Category_Id                  float64
Level1_Category_Name                 object
Level2_Category_Id                  float64
Level2_Category_Name                 object
Level3_Category_Id                  float64
Level3_Category_Name                 object
gender                               object
unique_id                             int64
type                                 object
month                                 int64
dayOfMonth                            int64
dayofWeek                            object
hour                                  int64
date                         datetime64[ns]
dtype: object


plt.figure(figsize=(16,4))

ax = sns.countplot(x="date", data=train,hue = 'gender',order = pd.date_range(start="2020-10-10",end="2020-12-12"))


plt.figure(figsize=(25,4))

g = sns.catplot(x="date", hue="type", col="gender",
                data=train, kind="count",
                height=10, aspect=1 ,order = pd.date_range(start="2020-11-10",end="2020-12-12"));

<Figure size 1800x288 with 0 Axes>


plt.figure(figsize=(25,4))

g = sns.catplot(x="date", hue="type", col="gender",
                data=train, kind="count",
                height=10, aspect=1 ,order = pd.date_range(start="2020-11-01",end="2020-12-01"));

<Figure size 1800x288 with 0 Axes>


plt.figure(figsize=(25,4))

g = sns.catplot(x="date", hue="product_gender", col="gender",
                data=train, kind="count",
                height=10, aspect=1 ,order = pd.date_range(start="2020-11-01",end="2020-12-01"));

<Figure size 1800x288 with 0 Axes>


g = sns.catplot(x="product_gender", hue="type", col="gender",
                data=train, kind="count",
                height=10, aspect=1);


fig, axes = plt.subplots(2, figsize=(8, 8))

axes[0].set_title('Product Gender for Female')
train[train['gender']=='F'].groupby(train.product_gender).size().plot(kind='pie', ax= axes[0], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[1].set_title('Product Gender for male')
train[train['gender']=='M'].groupby(train.product_gender).size().plot(kind='pie', ax= axes[1], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

plt.tight_layout(pad=3);


fig, axes = plt.subplots(2, figsize=(8, 8))

axes[0].set_title('User Action for Female')
train[train['gender']=='F'].groupby(train.user_action).size().plot(kind='pie', ax= axes[0], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[1].set_title('User Action for male')
train[train['gender']=='M'].groupby(train.user_action).size().plot(kind='pie', ax= axes[1], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

plt.tight_layout(pad=3);


train.businessunit.unique()

array(['Beyaz Eşya', 'Branded Shoes A', 'Gözlük A', 'İç Giyim', 'Kadın B',
       'PL Woman', 'Sağlık', 'Sofra & Mutfak', 'Branded Shoes B',
       'Salon &Mutfak Mobilya', 'Luxury', 'Çanta & Valiz', 'Cilt Bakım',
       'Kırtasiye & Ofis', 'Ev Dekorasyon', 'Elektrikli Ev Aletleri',
       'Saat A', 'Bahçe & Yapı Market & Hırdavat', 'Ev Gereçleri',
       'Ev Tekstil', 'Yatak Oda Mobilyası', 'Kadın A', 'Banyo', 'Parfüm',
       'TV & Görüntü Ses Sistemleri', 'Cep Telefonu & Aksesuarları',
       'Outdoor Equipment', 'Ev Bakım ve Temizlik', 'Konservatif',
       'Outdoor', 'Büyük Beden', 'Kişisel Bakım', 'Sportswear', 'Mayo',
       'Gözlük B', 'Oto Aksesuar', 'Vücut Bakım', 'Diğer Aksesuarlar',
       'Anne & Bebek Bakım', 'Sport Equipment', 'Erkek B', 'Erkek A',
       'GAS Casual', 'Gıda ve İçecek', 'Makyaj', 'Saat B', 'Takı',
       'Kids & Baby Fashion', 'Designer', 'Lüks & Tasarım', 'Hobi',
       'Books', 'Halı/Kilim', 'Oyuncak', 'Saç Bakım', 'Mücevher',
       'Digital Goods', 'GAS Denim', nan, 'PL Homewear', 'PL Designer',
       'PL Erkek', 'Ev Giyim', 'Aydınlatma', 'Bahçe Mobilyası',
       'Kişisel Bakım Aletleri', 'Ofis Mobilyası', 'Çocuk Gereç',
       'Bebek Bezi & Mendil', 'PL Beach', 'PL Party & Wedding',
       'Bilgisayar & Tablet & Aksesuarları', 'Fantezi Giyim', 'Petshop',
       'Oyun & Oyun Konsolları', 'Foto & Kamera', 'Aksesuar',
       'PL Ayakkabı', 'Çocuk', 'GAS', 'Elektronik', 'Mobilya',
       'Kitap & Kırtasiye & Yaşam', 'Ev'], dtype=object)


features = ['user_action'
           ,'sellingprice'
           ,'businessunit'
           ,'product_gender'
           ,'month'
           ,'dayOfMonth'
           ,'hour'
           ,'gender']

train_sample = train_sample[features]


from sklearn.ensemble import RandomForestClassifier

# Create a copy to work with
X = train_sample.copy()

# Save and drop labels
y = train_sample.gender
X = X.drop(columns = ['gender'], axis=1)

# fill NANs 
X = X.fillna(-999)

# Label encoder
for c in X.columns[X.dtypes == 'object']:
    X[c] = X[c].factorize()[0]


rf = RandomForestClassifier()
rf.fit(X,y)

RandomForestClassifier()


plt.plot(rf.feature_importances_)
plt.xticks(np.arange(X.shape[1]), X.columns.tolist(), rotation=90);


rf.score(X, y, sample_weight=None)

0.997600700493513


X.head(4).T


from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV


random_forest = RandomForestClassifier(random_state = 77)

param_grid = {
             'max_depth': np.arange(5,20),
             'min_samples_leaf': np.arange(1,10),
             'n_estimators': np.arange(50, 500)
             }

random_forest_cv = RandomizedSearchCV(random_forest, param_grid, n_iter= 100, scoring='accuracy', cv=10, verbose=1)
random_forest_cv.fit(X, y)

Fitting 10 folds for each of 100 candidates, totalling 1000 fits

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/var/folders/w7/prlcb1cx2rx2jwdcq4ftbmk40000gn/T/ipykernel_3454/3051463394.py in <module>
     11 
     12 random_forest_cv = RandomizedSearchCV(random_forest, param_grid, n_iter= 100, scoring='accuracy', cv=10, verbose=1)
---> 13 random_forest_cv.fit(X, y)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
    839                 return results
    840 
--> 841             self._run_search(evaluate_candidates)
    842 
    843             # multimetric is determined here because in the case of a callable

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
   1631     def _run_search(self, evaluate_candidates):
   1632         """Search n_iter candidates from param_distributions"""
-> 1633         evaluate_candidates(ParameterSampler(
   1634             self.param_distributions, self.n_iter,
   1635             random_state=self.random_state))

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params, cv, more_results)
    793                               n_splits, n_candidates, n_candidates * n_splits))
    794 
--> 795                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),
    796                                                        X, y,
    797                                                        train=train, test=test,

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self, iterable)
   1044                 self._iterating = self._original_iterator is not None
   1045 
-> 1046             while self.dispatch_one_batch(iterator):
   1047                 pass
   1048 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    859                 return False
    860             else:
--> 861                 self._dispatch(tasks)
    862                 return True
    863 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in _dispatch(self, batch)
    777         with self._lock:
    778             job_idx = len(self._jobs)
--> 779             job = self._backend.apply_async(batch, callback=cb)
    780             # A job can complete so quickly than its callback is
    781             # called before we get here, causing self._jobs to

~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in <listcomp>(.0)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py in __call__(self, *args, **kwargs)
    220     def __call__(self, *args, **kwargs):
    221         with config_context(**self.config):
--> 222             return self.function(*args, **kwargs)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    596             estimator.fit(X_train, **fit_params)
    597         else:
--> 598             estimator.fit(X_train, y_train, **fit_params)
    599 
    600     except Exception as e:

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in fit(self, X, y, sample_weight)
    385             # parallel_backend contexts set at a higher level,
    386             # since correctness does not rely on using threads.
--> 387             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
    388                              **_joblib_parallel_args(prefer='threads'))(
    389                 delayed(_parallel_build_trees)(

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self, iterable)
   1044                 self._iterating = self._original_iterator is not None
   1045 
-> 1046             while self.dispatch_one_batch(iterator):
   1047                 pass
   1048 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    859                 return False
    860             else:
--> 861                 self._dispatch(tasks)
    862                 return True
    863 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in _dispatch(self, batch)
    777         with self._lock:
    778             job_idx = len(self._jobs)
--> 779             job = self._backend.apply_async(batch, callback=cb)
    780             # A job can complete so quickly than its callback is
    781             # called before we get here, causing self._jobs to

~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py in <listcomp>(.0)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py in __call__(self, *args, **kwargs)
    220     def __call__(self, *args, **kwargs):
    221         with config_context(**self.config):
--> 222             return self.function(*args, **kwargs)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    167                                                         indices=indices)
    168 
--> 169         tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    170     else:
    171         tree.fit(X, y, sample_weight=sample_weight, check_input=False)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    901         """
    902 
--> 903         super().fit(
    904             X, y,
    905             sample_weight=sample_weight,

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    392                                            min_impurity_split)
    393 
--> 394         builder.build(self.tree_, X, y, sample_weight)
    395 
    396         if self.n_outputs_ == 1 and is_classifier(self):

KeyboardInterrupt:


from sklearn.svm import SVC

param_grid = {'C': [0.0001, 0.001, 0.01, 0.1, 1, 5, 10, 100],
             'kernel': ['linear','poly','rbf'],
             'degree': [5,6,7]}
 
grid = GridSearchCV(SVC(), param_grid, scoring='accuracy', cv=10, verbose=1)
 
# fitting the model for grid search
grid_fit = grid.fit(X, y)

Fitting 10 folds for each of 72 candidates, totalling 720 fits


model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 1000).fit(X1, y1)


# user specific feature generation

# userid - user action based statistics

user_vs_action= train.groupby(by=['unique_id', 'user_action'], as_index=False).size()
user_vs_action = user_vs_action.pivot_table('size', ['unique_id'], 'user_action')
user_vs_action.reset_index( drop=False, inplace=True ) 
user_vs_action = user_vs_action[['unique_id', 'favorite', 'order', 'search', 'basket', 'visit']]
user_vs_action = user_vs_action.fillna(0)

user_vs_action['total_transaction'] =  (user_vs_action['favorite'] 
                                        + user_vs_action['order'] 
                                        + user_vs_action['search'] 
                                        + user_vs_action['basket'] 
                                        + user_vs_action['visit'] )

for x in train.user_action.unique().tolist():
    user_vs_action[x + '_percent'] = (
    user_vs_action[x] / user_vs_action['total_transaction']) 
    
for x in train.user_action.unique().tolist():
    for y in train.user_action.unique().tolist():
        if x == y:
            continue
        user_vs_action[x + '_over_' + y] = (
        user_vs_action[x] / user_vs_action[y]) 

for x in train.user_action.unique().tolist():
    for y in train.user_action.unique().tolist():
        if x == y:
            continue
        user_vs_action[x + '_perc_times_' + y + '_perc'] = (
        user_vs_action[x + '_percent'] * user_vs_action[y + '_percent'])


user_vs_gender= train.groupby(by=['unique_id', 'gender'], as_index=False).size().drop(columns=['size'])
user_vs_action = pd.merge(user_vs_action, user_vs_gender, on = 'unique_id')

user_vs_action.replace([np.inf, -np.inf], np.nan, inplace=True)
X = user_vs_action.drop(columns = ['gender']).fillna(0)
y = user_vs_action.gender


# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.663937

0.6723033107867569


ax = plt.hist(x="total_transaction", data=user_vs_action[user_vs_action['total_transaction']<150], bins = 100, color = 'b')

plt.tight_layout(pad=3);


user_vs_action_pgender= train.groupby(by=['unique_id', 'user_action', 'product_gender'], as_index=False, dropna= False).size()

user_vs_action_pgender = user_vs_action_pgender.fillna('NotDefined')
user_vs_action_pgender = user_vs_action_pgender.pivot_table('size', ['unique_id'], ['user_action', 'product_gender'])

list = []
for x in user_vs_action_pgender.columns:
    list.append(x[0] + '_'+ x[1])
user_vs_action_pgender.columns = list

user_vs_action_pgender.reset_index( drop=False, inplace=True ) 

user_vs_action_pgender = user_vs_action_pgender.fillna(0)



user_features = pd.merge(user_vs_action, user_vs_action_pgender, on = 'unique_id')

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    user_features[x + '_percent'] = (
    user_features[x] / user_features[x.split('_')[0]])

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        user_features[x + '_over_' + y] = (
        user_features[x] / user_features[y]) 

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        user_features[x + '_perc_times_' + y + '_perc'] = (
        user_features[x + '_percent'] * user_features[y + '_percent']) 

user_features.replace([np.inf, -np.inf], np.nan, inplace=True)
X = user_features.drop(columns = ['gender']).fillna(0)
y = user_features.gender


# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983

0.7993948024207903


from sklearn.decomposition import PCA
pca = PCA()
Xt = pca.fit_transform(X_scaled)


pca.explained_variance_ratio_.reshape(852,1).cumsum()

array([0.07570738, 0.13131086, 0.17940537, 0.20319824, 0.22390448,
       0.24441229, 0.26129579, 0.27765459, 0.29344099, 0.30860345,
       0.32334988, 0.33695636, 0.34974458, 0.3622137 , 0.37370532,
       0.38457572, 0.39477399, 0.40475181, 0.41435249, 0.42359927,
       0.43257409, 0.44115778, 0.44952574, 0.45777845, 0.46576068,
       0.47345407, 0.48097637, 0.48839897, 0.49557162, 0.50271361,
       0.50953491, 0.51598401, 0.52229467, 0.52849772, 0.5346031 ,
       0.54050175, 0.54632795, 0.55196417, 0.55749892, 0.56294707,
       0.56827944, 0.57356867, 0.57863747, 0.5836396 , 0.58847755,
       0.59326386, 0.5979297 , 0.60250192, 0.60703493, 0.6114066 ,
       0.61572714, 0.61996073, 0.62412714, 0.62826222, 0.63229455,
       0.63628427, 0.64021277, 0.64399518, 0.64769117, 0.65137186,
       0.6549849 , 0.65853473, 0.66203957, 0.66548104, 0.66881941,
       0.67211794, 0.67534144, 0.67850795, 0.68163039, 0.68471084,
       0.68775591, 0.69075801, 0.69370114, 0.69653043, 0.69932006,
       0.70207909, 0.70481611, 0.70751139, 0.71019351, 0.71283542,
       0.71541131, 0.71792528, 0.72042302, 0.72287472, 0.72527762,
       0.72766994, 0.73003997, 0.73236701, 0.73468296, 0.73696545,
       0.73923549, 0.74146614, 0.7436673 , 0.74582256, 0.7479694 ,
       0.7500814 , 0.75217459, 0.7542488 , 0.7563051 , 0.75834159,
       0.76034957, 0.76232703, 0.76426059, 0.76617583, 0.76807557,
       0.76995728, 0.77182935, 0.77367692, 0.77547531, 0.77725424,
       0.77902112, 0.78077285, 0.78249646, 0.78420473, 0.78590554,
       0.78758589, 0.78925718, 0.79090674, 0.79252351, 0.79413206,
       0.79573054, 0.79731917, 0.7988848 , 0.80043004, 0.8019742 ,
       0.80349645, 0.80501544, 0.80650761, 0.80798535, 0.80945354,
       0.81089961, 0.81233976, 0.81374794, 0.8151412 , 0.81651238,
       0.81788094, 0.81923818, 0.82058829, 0.82193186, 0.82324599,
       0.82455494, 0.82585194, 0.82713793, 0.82841495, 0.82968651,
       0.83095233, 0.83220719, 0.83343915, 0.83466583, 0.83587675,
       0.83708423, 0.8382805 , 0.83945902, 0.84063084, 0.84179119,
       0.84294887, 0.84409712, 0.84523769, 0.84637076, 0.8474989 ,
       0.84860737, 0.84970904, 0.85080708, 0.85189005, 0.85296661,
       0.85403894, 0.85510659, 0.85615874, 0.8572075 , 0.85823438,
       0.85925269, 0.86026369, 0.86127371, 0.86227275, 0.86326444,
       0.86424574, 0.86521476, 0.86617953, 0.86714083, 0.86809286,
       0.8690391 , 0.86997489, 0.87090139, 0.87182188, 0.87273895,
       0.87364437, 0.87454464, 0.87543937, 0.8763179 , 0.87719116,
       0.87805827, 0.87891758, 0.87977168, 0.8806242 , 0.88147105,
       0.88231095, 0.88314735, 0.88397826, 0.88480729, 0.88563016,
       0.88644487, 0.88725511, 0.88805794, 0.88885317, 0.88964379,
       0.89043274, 0.89121161, 0.89198415, 0.89274983, 0.8935127 ,
       0.89427373, 0.89502923, 0.89577536, 0.89651688, 0.89725059,
       0.89798221, 0.89871185, 0.89943524, 0.90015561, 0.90086958,
       0.90157507, 0.90227767, 0.90297751, 0.90366997, 0.90435946,
       0.90504117, 0.90571994, 0.90639418, 0.90706119, 0.90772636,
       0.90838821, 0.90904449, 0.90970023, 0.9103512 , 0.91099634,
       0.91163862, 0.91227505, 0.91290873, 0.91353934, 0.91416771,
       0.91479308, 0.91541041, 0.91602699, 0.91663687, 0.91724298,
       0.91784239, 0.91843931, 0.91903467, 0.91962812, 0.92021003,
       0.92078774, 0.92136174, 0.92193334, 0.92250367, 0.92307264,
       0.923635  , 0.92419512, 0.92475459, 0.92530862, 0.9258583 ,
       0.9264059 , 0.92695193, 0.92749506, 0.92803703, 0.92857562,
       0.92911152, 0.92964316, 0.93017394, 0.93069774, 0.93121979,
       0.93173734, 0.93225225, 0.93276632, 0.93327491, 0.93378102,
       0.93428405, 0.93478009, 0.93527512, 0.93576942, 0.9362586 ,
       0.9367462 , 0.93723286, 0.93771447, 0.93819463, 0.9386726 ,
       0.93914515, 0.93961304, 0.94007972, 0.94054476, 0.9410075 ,
       0.9414676 , 0.94192465, 0.94237967, 0.94283233, 0.9432806 ,
       0.94372535, 0.94416772, 0.94460577, 0.94503993, 0.9454728 ,
       0.9459029 , 0.94633089, 0.94675705, 0.94718177, 0.94760363,
       0.94802449, 0.94843934, 0.94885364, 0.94926647, 0.94967728,
       0.95008479, 0.95049094, 0.95089452, 0.95129186, 0.95168614,
       0.95207981, 0.95247276, 0.95286271, 0.95325134, 0.95363802,
       0.95402406, 0.95440466, 0.95478431, 0.95516035, 0.95553482,
       0.95590632, 0.95627606, 0.95664366, 0.95701053, 0.95737481,
       0.95773805, 0.95809746, 0.95845374, 0.95880822, 0.95915957,
       0.95951045, 0.9598598 , 0.96020621, 0.96055233, 0.96089518,
       0.96123546, 0.96157538, 0.96191306, 0.96224816, 0.96258134,
       0.96291064, 0.9632392 , 0.96356744, 0.96389256, 0.9642167 ,
       0.96453676, 0.96485331, 0.96516534, 0.96547699, 0.96578683,
       0.96609469, 0.96640031, 0.96670512, 0.96700797, 0.96730835,
       0.96760721, 0.96790473, 0.9682    , 0.96849356, 0.96878503,
       0.96907355, 0.96936179, 0.96964799, 0.96993241, 0.97021314,
       0.97049248, 0.97077056, 0.97104746, 0.97132042, 0.97159312,
       0.97186328, 0.97213288, 0.9724008 , 0.97266664, 0.97292934,
       0.97319024, 0.97345004, 0.97370872, 0.97396462, 0.97421937,
       0.97447244, 0.97472492, 0.97497517, 0.97522478, 0.97547058,
       0.97571461, 0.97595592, 0.97619714, 0.97643685, 0.97667435,
       0.97690908, 0.97714376, 0.9773774 , 0.97760775, 0.97783455,
       0.97806069, 0.97828654, 0.97850858, 0.9787291 , 0.97894879,
       0.97916523, 0.97938165, 0.97959539, 0.97980815, 0.98002014,
       0.98023113, 0.98043972, 0.98064689, 0.98085288, 0.98105661,
       0.98125954, 0.98146192, 0.98166291, 0.98186058, 0.98205655,
       0.9822523 , 0.98244719, 0.98264103, 0.9828321 , 0.98302261,
       0.98321002, 0.98339554, 0.98357979, 0.98376137, 0.98394187,
       0.98412158, 0.9842994 , 0.98447589, 0.98465082, 0.98482511,
       0.98499708, 0.9851679 , 0.98533682, 0.98550446, 0.98567029,
       0.98583563, 0.98599828, 0.9861593 , 0.98631907, 0.98647698,
       0.98663273, 0.98678742, 0.98694089, 0.9870929 , 0.98724224,
       0.98739102, 0.98753866, 0.98768422, 0.98782912, 0.98797344,
       0.98811693, 0.98825822, 0.98839822, 0.98853807, 0.98867605,
       0.98881204, 0.98894775, 0.98908129, 0.9892141 , 0.98934533,
       0.98947562, 0.98960496, 0.98973326, 0.98986112, 0.9899873 ,
       0.9901122 , 0.99023657, 0.99036032, 0.9904833 , 0.99060465,
       0.9907254 , 0.99084559, 0.990964  , 0.99108087, 0.99119672,
       0.99131111, 0.99142519, 0.99153872, 0.99165097, 0.99176179,
       0.991872  , 0.99198068, 0.99208905, 0.99219509, 0.99229871,
       0.99240196, 0.99250492, 0.99260707, 0.99270837, 0.99280846,
       0.9929073 , 0.9930054 , 0.99310239, 0.99319893, 0.99329521,
       0.99339081, 0.99348579, 0.99357973, 0.99367295, 0.99376433,
       0.99385464, 0.9939448 , 0.99403372, 0.99412184, 0.99420965,
       0.99429636, 0.99438286, 0.99446851, 0.99455324, 0.99463777,
       0.99472153, 0.99480382, 0.9948852 , 0.99496616, 0.99504657,
       0.99512603, 0.99520516, 0.99528335, 0.99536044, 0.99543693,
       0.99551191, 0.99558656, 0.99565959, 0.99573228, 0.99580395,
       0.99587474, 0.99594469, 0.99601349, 0.99608174, 0.99614962,
       0.9962166 , 0.99628285, 0.99634856, 0.99641339, 0.99647683,
       0.99653993, 0.99660178, 0.99666349, 0.9967249 , 0.99678536,
       0.99684458, 0.99690359, 0.99696243, 0.99702091, 0.99707834,
       0.99713531, 0.99719195, 0.99724817, 0.99730331, 0.99735814,
       0.99741227, 0.99746585, 0.99751926, 0.99757172, 0.9976235 ,
       0.99767432, 0.99772449, 0.997774  , 0.99782317, 0.99787146,
       0.99791947, 0.99796682, 0.9980136 , 0.99805945, 0.99810496,
       0.99814994, 0.99819396, 0.99823753, 0.9982807 , 0.99832304,
       0.99836478, 0.99840633, 0.99844778, 0.99848782, 0.9985273 ,
       0.9985662 , 0.99860442, 0.99864176, 0.99867865, 0.99871516,
       0.99875142, 0.99878733, 0.99882234, 0.99885695, 0.99889104,
       0.9989247 , 0.99895814, 0.99899081, 0.99902331, 0.99905506,
       0.99908681, 0.99911762, 0.99914804, 0.99917836, 0.99920772,
       0.99923652, 0.9992646 , 0.99929234, 0.99931948, 0.99934614,
       0.99937259, 0.99939856, 0.9994238 , 0.99944888, 0.99947382,
       0.99949861, 0.99952239, 0.99954588, 0.99956854, 0.99959111,
       0.99961275, 0.99963384, 0.9996544 , 0.99967415, 0.99969334,
       0.99971187, 0.99972996, 0.99974765, 0.99976523, 0.99978263,
       0.99979957, 0.99981611, 0.99983242, 0.99984812, 0.99986339,
       0.99987844, 0.99989239, 0.99990575, 0.9999189 , 0.99993149,
       0.99994334, 0.99995402, 0.99996407, 0.99997311, 0.99998086,
       0.99998665, 0.99999229, 0.99999581, 0.99999828, 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        ])


print(pca.explained_variance_ratio_)

[7.57073799e-02 5.56034761e-02 4.80945124e-02 2.37928765e-02
 2.07062396e-02 2.05078032e-02 1.68835015e-02 1.63587982e-02
 1.57864027e-02 1.51624624e-02 1.47464263e-02 1.36064779e-02
 1.27882211e-02 1.24691267e-02 1.14916205e-02 1.08703998e-02
 1.01982610e-02 9.97782668e-03 9.60067492e-03 9.24678096e-03
 8.97482363e-03 8.58369147e-03 8.36795443e-03 8.25270921e-03
 7.98223370e-03 7.69339083e-03 7.52229772e-03 7.42260168e-03
 7.17265371e-03 7.14198304e-03 6.82129913e-03 6.44909912e-03
 6.31066820e-03 6.20304573e-03 6.10537574e-03 5.89865593e-03
 5.82619801e-03 5.63621612e-03 5.53475611e-03 5.44815017e-03
 5.33237180e-03 5.28922606e-03 5.06879926e-03 5.00212628e-03
 4.83795606e-03 4.78630687e-03 4.66584667e-03 4.57221329e-03
 4.53301120e-03 4.37166834e-03 4.32053724e-03 4.23359077e-03
 4.16641154e-03 4.13508047e-03 4.03233013e-03 3.98972620e-03
 3.92849859e-03 3.78241027e-03 3.69598294e-03 3.68069564e-03
 3.61304027e-03 3.54982863e-03 3.50483783e-03 3.44147251e-03
 3.33836451e-03 3.29853207e-03 3.22350168e-03 3.16651093e-03
 3.12244139e-03 3.08045055e-03 3.04507203e-03 3.00209386e-03
 2.94313597e-03 2.82929110e-03 2.78962996e-03 2.75902246e-03
 2.73701985e-03 2.69528238e-03 2.68212340e-03 2.64190316e-03
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pd_y = pd.DataFrame(y)
pd_y['label'] = (pd_y['gender'] == 'F')
pd_y['label'] = (pd_y['label']).astype(int)


plt.figure(figsize=(16,8))

plot = plt.scatter(Xt[:,0], Xt[:,1], c=pd_y['label'])
plt.legend(handles=plot.legend_elements()[0], labels=['F', 'M'])
plt.show()


roes


pd_x_scaled = pd.DataFrame(X_scaled)
pd_x_scaled.columns = X.columns
pd_x_scaled


data = pd.concat([pd_x_scaled, pd_y['label']],axis = 1)


clf = setup(data, target='label', fold_shuffle=True)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/w7/prlcb1cx2rx2jwdcq4ftbmk40000gn/T/ipykernel_21621/3521948135.py in <module>
----> 1 clf = setup(data, target='label', fold_shuffle=True)

~/opt/anaconda3/lib/python3.9/site-packages/pycaret/regression.py in setup(data, target, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, transform_target, transform_target_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs)
    569         log_plots = ["residuals", "error", "feature"]
    570 
--> 571     return pycaret.internal.tabular.setup(
    572         ml_usecase="regression",
    573         available_plots=available_plots,

~/opt/anaconda3/lib/python3.9/site-packages/pycaret/internal/tabular.py in setup(data, target, ml_usecase, available_plots, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, fix_imbalance, fix_imbalance_method, transform_target, transform_target_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs, display)
   1743     logger.info(f"display_container: {len(display_container)}")
   1744 
-> 1745     logger.info(str(prep_pipe))
   1746     logger.info("setup() succesfully completed......................................")
   1747 

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py in __repr__(self, N_CHAR_MAX)
    258             n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW)
    259 
--> 260         repr_ = pp.pformat(self)
    261 
    262         # Use bruteforce ellipsis when there are a lot of non-blank characters

~/opt/anaconda3/lib/python3.9/pprint.py in pformat(self, object)
    151     def pformat(self, object):
    152         sio = _StringIO()
--> 153         self._format(object, sio, 0, 0, {}, 0)
    154         return sio.getvalue()
    155 

~/opt/anaconda3/lib/python3.9/pprint.py in _format(self, object, stream, indent, allowance, context, level)
    168             self._readable = False
    169             return
--> 170         rep = self._repr(object, context, level)
    171         max_width = self._width - indent - allowance
    172         if len(rep) > max_width:

~/opt/anaconda3/lib/python3.9/pprint.py in _repr(self, object, context, level)
    429 
    430     def _repr(self, object, context, level):
--> 431         repr, readable, recursive = self.format(object, context.copy(),
    432                                                 self._depth, level)
    433         if not readable:

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/_pprint.py in format(self, object, context, maxlevels, level)
    178 
    179     def format(self, object, context, maxlevels, level):
--> 180         return _safe_repr(object, context, maxlevels, level,
    181                           changed_only=self._changed_only)
    182 

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/_pprint.py in _safe_repr(object, context, maxlevels, level, changed_only)
    434             krepr, kreadable, krecur = saferepr(
    435                 k, context, maxlevels, level, changed_only=changed_only)
--> 436             vrepr, vreadable, vrecur = saferepr(
    437                 v, context, maxlevels, level, changed_only=changed_only)
    438             append("%s=%s" % (krepr.strip("'"), vrepr))

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/_pprint.py in _safe_repr(object, context, maxlevels, level, changed_only)
    403         level += 1
    404         for o in object:
--> 405             orepr, oreadable, orecur = _safe_repr(
    406                 o, context, maxlevels, level, changed_only=changed_only)
    407             append(orepr)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/_pprint.py in _safe_repr(object, context, maxlevels, level, changed_only)
    403         level += 1
    404         for o in object:
--> 405             orepr, oreadable, orecur = _safe_repr(
    406                 o, context, maxlevels, level, changed_only=changed_only)
    407             append(orepr)

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/_pprint.py in _safe_repr(object, context, maxlevels, level, changed_only)
    425             params = _changed_params(object)
    426         else:
--> 427             params = object.get_params(deep=False)
    428         components = []
    429         append = components.append

~/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py in get_params(self, deep)
    193         out = dict()
    194         for key in self._get_param_names():
--> 195             value = getattr(self, key)
    196             if deep and hasattr(value, 'get_params'):
    197                 deep_items = value.get_params().items()

AttributeError: 'Simple_Imputer' object has no attribute 'fill_value_categorical'


compare_models()

GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
                          init=None, learning_rate=0.1, loss='ls', max_depth=3,
                          max_features=None, max_leaf_nodes=None,
                          min_impurity_decrease=0.0, min_impurity_split=None,
                          min_samples_leaf=1, min_samples_split=2,
                          min_weight_fraction_leaf=0.0, n_estimators=100,
                          n_iter_no_change=None, random_state=2604,
                          subsample=1.0, tol=0.0001, validation_fraction=0.1,
                          verbose=0, warm_start=False)


model = create_model('gbr')


tuned_gbr = tune_model(model)


plot_model(tuned_gbr)


plot_model(tuned_gbr, plot = 'error')


plot_model(tuned_gbr, plot = 'feature')


evaluate_model(tuned_gbr)


predictions = predict_model(tuned_gbr)


# test set generation 

user_vs_action= test.groupby(by=['unique_id', 'user_action'], as_index=False).size()
user_vs_action = user_vs_action.pivot_table('size', ['unique_id'], 'user_action')
user_vs_action.reset_index( drop=False, inplace=True ) 
user_vs_action = user_vs_action[['unique_id', 'favorite', 'order', 'search', 'basket', 'visit']]
user_vs_action = user_vs_action.fillna(0)

user_vs_action['total_transaction'] =  (user_vs_action['favorite'] 
                                        + user_vs_action['order'] 
                                        + user_vs_action['search'] 
                                        + user_vs_action['basket'] 
                                        + user_vs_action['visit'] )

for x in train.user_action.unique().tolist():
    user_vs_action[x + '_percent'] = (
    user_vs_action[x] / user_vs_action['total_transaction']) 
    
for x in train.user_action.unique().tolist():
    for y in train.user_action.unique().tolist():
        if x == y:
            continue
        user_vs_action[x + '_over_' + y] = (
        user_vs_action[x] / user_vs_action[y]) 

for x in train.user_action.unique().tolist():
    for y in train.user_action.unique().tolist():
        if x == y:
            continue
        user_vs_action[x + '_perc_times_' + y + '_perc'] = (
        user_vs_action[x + '_percent'] * user_vs_action[y + '_percent']) 
        


user_vs_action.replace([np.inf, -np.inf], np.nan, inplace=True)
X = user_vs_action.fillna(0)

user_vs_action_pgender= test.groupby(by=['unique_id', 'user_action', 'product_gender'], as_index=False, dropna= False).size()

user_vs_action_pgender = user_vs_action_pgender.fillna('NotDefined')
user_vs_action_pgender = user_vs_action_pgender.pivot_table('size', ['unique_id'], ['user_action', 'product_gender'])

list = []
for x in user_vs_action_pgender.columns:
    list.append(x[0] + '_'+ x[1])
user_vs_action_pgender.columns = list

user_vs_action_pgender.reset_index( drop=False, inplace=True ) 

user_vs_action_pgender = user_vs_action_pgender.fillna(0)



user_features = pd.merge(user_vs_action, user_vs_action_pgender, on = 'unique_id')

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    user_features[x + '_percent'] = (
    user_features[x] / user_features[x.split('_')[0]])

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        user_features[x + '_over_' + y] = (
        user_features[x] / user_features[y]) 

for x in user_vs_action_pgender.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        user_features[x + '_perc_times_' + y + '_perc'] = (
        user_features[x + '_percent'] * user_features[y + '_percent']) 

user_features.replace([np.inf, -np.inf], np.nan, inplace=True)
X_test = user_features.fillna(0)


X_test = scaler.transform(X)
pd_x_test_scaled = pd.DataFrame(X_test)
pd_x_test_scaled.columns = X.columns
pd_x_test_scaled


predictions = predict_model(tuned_gbr, data = pd_x_test_scaled)


preds = pd.concat([X[['unique_id']], predictions[['Label']]], axis = 1)


results = pd.merge(test_ids, preds, on = 'unique_id', how= 'left')


converted_list = [str(element) for element in results.Label.tolist()]


",".join(converted_list)

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a_list = ["a", "b", "c"]
joined_string = ",".join(a_list)


marker = 0
for i in range(0, len(X_scaled)):
    if round(X_scaled[i][0],6) == 0.032805:
        marker = i

X.iloc[marker,0]

2841


pd.reset_option('display.max_columns')
pd.reset_option('display.max_rows')

predictions[predictions['is_true_predicted']==0]


ax = sns.scatterplot(x="visit_Kadın_percent", y= "Label", data=predictions, hue = 'label')
plt.tight_layout(pad=2);


ax = sns.scatterplot(x="search_Erkek_percent", y= "Label", data=predictions, hue = 'label')
plt.tight_layout(pad=2);


sns.pairplot(predictions[['visit_Kadın_percent', 'search_Erkek_percent', 'search_Kadın_percent', 'label']], hue='label');


predictions['is_true_predicted'] = predictions['label'] == ((predictions['Label']> 0.5))


predictions[predictions['total_transaction']< -0.2]


ax = sns.scatterplot(x="total_transaction", y= "visit_Kadın_percent", data=predictions[predictions['total_transaction']< -0.2], hue = 'is_true_predicted')
plt.tight_layout(pad=2);


sns.pairplot(predictions[['visit_Kadın_percent', 'search_Erkek_percent', 'search_Kadın_percent','total_transaction', 'is_true_predicted']], hue='is_true_predicted');


fig, axes = plt.subplots(3, figsize=(8, 8))

axes[0].set_title('Accuracy for Female')
predictions[predictions['label']==1].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[0], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[1].set_title('Accuracy for male')
predictions[predictions['label']==0].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[1], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[2].set_title('Accuracy')
predictions.groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[2], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

plt.tight_layout(pad=3);


predictions['total_transaction'].describe()

count    1686.000000
mean        0.010010
std         0.977772
min        -0.258574
25%        -0.252220
50%        -0.225616
75%        -0.101730
max        15.405704
Name: total_transaction, dtype: float64


fig, axes = plt.subplots(4, figsize=(8, 8))

axes[0].set_title('Accuracy for high transactions')
predictions[predictions['total_transaction']> -0.101730].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[0], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[1].set_title('Accuracy for users with very low transaction number')
predictions[predictions['total_transaction']< -0.252220].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[1], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[2].set_title('Accuracy for medium transactions')
predictions[predictions['total_transaction']> -0.225616].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[2], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[3].set_title('Accuracy for medium transactions')
predictions[predictions['total_transaction']< -0.225616].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[3], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

plt.tight_layout(pad=3);


fig, axes = plt.subplots(3, figsize=(8, 8))

axes[0].set_title('Accuracy for Female')
predictions[(predictions['label']==1) & (predictions['total_transaction']< -0.252220)].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[0], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[1].set_title('Accuracy for male')
predictions[(predictions['label']==0) & ( predictions['total_transaction']< -0.252220)].groupby(predictions.is_true_predicted).size().plot(kind='pie', ax= axes[1], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

axes[2].set_title('Distribution of Gender')
predictions[predictions['total_transaction']< -0.252220].groupby(predictions.label).size().plot(kind='pie', ax= axes[2], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.axis('equal')

plt.tight_layout(pad=3);


user_vs_action_in_promotion = pd.merge(user_vs_action_in_promotion, user_features[['gender', 'unique_id']], on = 'unique_id', how = 'left')


user_vs_action_in_promotion.replace([np.inf, -np.inf], np.nan, inplace=True)
X = user_vs_action_in_promotion.drop(columns = ['gender']).fillna(0)
y = user_vs_action_in_promotion.gender

# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983

0.6548593805624777


v1_and_v2 = pd.merge(user_vs_action, user_vs_action_in_promotion, on = 'unique_id', how = 'left')


feature_list_1 =  user_vs_action.columns.tolist()

feature_list_1.remove("gender")


feature_list_1 =  user_vs_action_in_promotion.columns.tolist()

feature_list_1.remove("gender")


v1_and_v2.replace([np.inf, -np.inf], np.nan, inplace=True)
#X = v1_and_v2.drop(columns = ['gender_x', 'gender_y']).fillna(0)
X = v1_and_v2[feature_list_1].fillna(0)
y = v1_and_v2.gender_x

# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983

0.663937344250623


# promotion days analysis

# promotion 1 = 09-12 kasım indirimleri
# promotion 2 = 25-26-27 kasım indirimleri


user_vs_action_pgender_in_promotion = train.copy()

user_vs_action_pgender_in_promotion["in_promotion"]  = user_vs_action_pgender_in_promotion["date"].isin(["2020-11-09", "2020-11-10", "2020-11-11", "2020-11-12", "2020-11-25", "2020-11-26", "2020-11-27" ])


user_vs_action_pgender_in_promotion= user_vs_action_pgender_in_promotion.groupby(by=['unique_id','in_promotion', 'product_gender'], as_index=False, dropna = False).size()
user_vs_action_pgender_in_promotion.product_gender = user_vs_action_pgender_in_promotion.product_gender.fillna('NotDefined')

user_vs_action_pgender_in_promotion = user_vs_action_pgender_in_promotion.pivot_table('size', ['unique_id'], ['product_gender','in_promotion'])

list = []
for x in user_vs_action_pgender_in_promotion.columns:
    str_x = 'no_promo'
    if x[1]:
        str_x = "promo"       
    list.append(x[0] + '_'+ str_x )
user_vs_action_pgender_in_promotion.columns = list

v1_v2 = user_vs_action_pgender_in_promotion.copy()

v1_v2 = v1_v2.fillna(0)

for x in user_vs_action_pgender_in_promotion.columns:
    if x == 'unique_id':
        continue
    if x.split('_')[1] == 'no':
        v1_v2[x + '_percent'] = (
    v1_v2[x] / (v1_v2[x] +  v1_v2[x.split('_')[0] + '_promo']))
        
    if x.split('_')[1] == 'promo':
        v1_v2[x + '_percent'] = (
    v1_v2[x] / (v1_v2[x] +  v1_v2[x.split('_')[0] + '_no_promo']))


v1_v2


for x in user_vs_action_pgender_in_promotion.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender_in_promotion.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        v1_v2[x + '_over_' + y] = (
        v1_v2[x] / v1_v2[y]) 

for x in user_vs_action_pgender_in_promotion.columns:
    if x == 'unique_id':
        continue
    for y in user_vs_action_pgender_in_promotion.columns:
        if y == 'unique_id':
            continue
        if x == y:
            continue
        v1_v2[x + '_perc_times_' + y + '_perc'] = (
        v1_v2[x + '_percent'] * v1_v2[y + '_percent'])

/var/folders/w7/prlcb1cx2rx2jwdcq4ftbmk40000gn/T/ipykernel_9122/2035909921.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  v1_v2[x + '_perc_times_' + y + '_perc'] = (


v1_v2_ = pd.merge(user_features, v1_v2, on= 'unique_id', how = 'left')


v1_v2_['total_transaction_no_promo'] =  (v1_v2_['Erkek_no_promo'] 
                                        + v1_v2_['Kadın_no_promo'] 
                                        + v1_v2_['NotDefined_no_promo'] 
                                        + v1_v2_['Unisex_no_promo'] )
v1_v2_['total_transaction_promo'] =  (v1_v2_['Erkek_promo'] 
                                        + v1_v2_['Kadın_promo'] 
                                        + v1_v2_['NotDefined_promo'] 
                                        + v1_v2_['Unisex_promo'] )
v1_v2_['promo_percent'] = v1_v2_['total_transaction_promo'] / v1_v2_['total_transaction']
v1_v2_['no_promo_percent'] = v1_v2_['total_transaction_no_promo'] / v1_v2_['total_transaction']


feature_list_1 =  user_features.columns.tolist()

feature_list_1.remove("gender")


feature_list_2 = feature_list_1 + ['Erkek_no_promo_percent',
 'Erkek_promo_percent',
 'Kadın_no_promo_percent',
 'Kadın_promo_percent',
 'NotDefined_no_promo_percent',
 'NotDefined_promo_percent',
 'Unisex_no_promo_percent',
 'Unisex_promo_percent',
'promo_percent',
 'no_promo_percent']


v1_v2_.replace([np.inf, -np.inf], np.nan, inplace=True)
#X = v1_v2_.drop(columns = ['gender']).fillna(0)
X = v1_v2_[feature_list_1].fillna(0)
y = v1_v2_.gender

# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983

0.7981488074047703


v1_v2_.replace([np.inf, -np.inf], np.nan, inplace=True)
X = v1_v2_.drop(columns = ['gender']).fillna(0)
#X = v1_v2_[feature_list_1].fillna(0)
y = v1_v2_.gender

# logistic regression 

from sklearn.linear_model import LogisticRegressionCV
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983

0.8031327874688501


from sklearn.decomposition import PCA
# Make an instance of the Model
pca = PCA(n_components = 20)
pca.fit(X_scaled)

X_scaled = pca.transform(X_scaled)


X_scaled.shape

model1 = LogisticRegressionCV(cv=10, random_state=0,
                              max_iter = 3000).fit(X_scaled, y)

model1.score(X_scaled,y) # 0.7983


train.head(2).T


products = train[['Level1_Category_Name',
                          'Level2_Category_Name',
                          'Level3_Category_Name',
                          'product_gender',
                         'contentid',
                         'product_name',
                         'brand_name',
                         'businessunit']].drop_duplicates()


train_filtered = pd.concat([train[train['gender'] == 'M'],train[train['gender'] == 'F'].sample(frac = 0.1944)], axis = 0)


# Change: groupby state_office and divide by sum
(train_filtered
 .groupby([
          'product_gender',
           'gender'], dropna = False)
 .agg({"time_stamp": pd.Series.nunique})
 .groupby([
          'product_gender'])
 .apply(lambda x: x / float(x.sum())))


['Level1_Category_Name']=='Anne & Bebek & Çocuk')
Level2_Category_Name = Bebek Giyim


# Change: groupby state_office and divide by sum
(train_filtered
 .groupby(['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'product_gender',
           'gender'], dropna = False)
 .agg({"time_stamp": pd.Series.nunique})
 .groupby(['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'product_gender'])
 .apply(lambda x: x / float(x.sum())))


# Change: groupby state_office and divide by sum
(train_filtered
 .groupby(['Level1_Category_Name',
          'product_gender',
           'gender'], dropna = False)
 .agg({"time_stamp": pd.Series.nunique})
 .groupby(['Level1_Category_Name',
          'product_gender'])
 .apply(lambda x: x / float(x.sum())))


products[products['Level3_Category_Name'] == 'Bot & Bootie']


train[train['contentid']==48168289]


train_filtered[train_filtered['Level3_Category_Name'] == 'Yatırımlık Altın Bilezik'].groupby('gender').size()

gender
F    763
M     77
dtype: int64


gender_stats = (train_filtered
 .groupby(['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender',
           'gender'], dropna = False)
 .agg({"time_stamp": pd.Series.nunique})
 .groupby(['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',
          'product_gender'], dropna = False,
                    as_index=False)
 .apply(lambda x: x / float(x.sum()))
               )
gender_stats = gender_stats.reset_index().fillna('NotDefined')
gender_stats = gender_stats[gender_stats['gender']=='M']


gender_stats


cols = ['businessunit',
        'Level1_Category_Name',
        'Level2_Category_Name',
        'Level3_Category_Name',
        'product_gender',
        'gender']
cols[0:len(cols)-1]

['businessunit',
 'Level1_Category_Name',
 'Level2_Category_Name',
 'Level3_Category_Name',
 'product_gender']


cols[0] +"_" +  cols[1].split("_")[0]

'businessunit_Level1'


col_name = "1"
for i in range(len(cols[0:len(cols)-2])):
        col_name = col_name + "_" + cols[i].split("_")[0]
col_name

'1_businessunit_Level1_Level2_Level3'


def generate_data(train_filtered, cols):
    gender_stats = (train_filtered
     .groupby(cols, dropna = False)
     .agg({"time_stamp": pd.Series.nunique})
     .groupby(cols[0:len(cols)-1], dropna = False,
                        as_index=False)
     .apply(lambda x: x / float(x.sum()))
                   )
    gender_stats = gender_stats.reset_index().fillna('NotDefined')
    gender_stats = gender_stats[gender_stats['gender']=='M'].drop(columns=['gender'])
    col_name = "1"
    for i in range(len(cols[0:len(cols)-2])):
            col_name = col_name + "_" + cols[i].split("_")[0]
    gender_stats.rename({'time_stamp': col_name}, axis=1, inplace=True)
    return gender_stats


cols.remove(cols[len(cols)-3])


cols = ['businessunit',
        'Level1_Category_Name',
        'Level2_Category_Name',
        'Level3_Category_Name',
        'product_gender',
        'gender']

for i in range(len(cols)):   
    if len(cols) == 2:
        break
    print(i)
    j_data = generate_data(train_filtered, cols)
    product_counts = pd.merge(product_counts, j_data, on = cols[0:len(cols)-1], how = 'left')
    cols.remove(cols[len(cols)-3])


cols = [
        'Level1_Category_Name',
        'Level2_Category_Name',
        'Level3_Category_Name',
        'businessunit',
        'product_gender',
        'gender']

for i in range(len(cols)):  
    cols.remove(cols[len(cols)-3])
    if len(cols) == 2:
        break
    print(i)
    j_data = generate_data(train_filtered, cols)
    product_counts = pd.merge(product_counts, j_data, on = cols[0:len(cols)-1], how = 'left')

0
1
2


product_counts


# Change: groupby state_office and divide by sum
product_counts = (products
 .groupby(['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender'], dropna = False,
                    as_index=False)
 .agg({ "contentid": pd.Series.nunique})
).fillna('NotDefined')
product_counts.columns = ['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender',
          'product_count']


def generate_counts(products, cols):
    product_counts = (products
             .groupby(cols[0:len(cols)-1], dropna = False,
                                as_index=False)
             .agg({ "contentid": pd.Series.nunique})
            ).fillna('NotDefined')
    product_counts.columns = cols
    col_name = "count"
    for i in range(len(cols[0:len(cols)-2])):
            col_name = col_name + "_" + cols[i].split("_")[0]
    product_counts.rename({'product_count': col_name}, axis=1, inplace=True)
    return product_counts


cols = [
        'Level1_Category_Name',
        'Level2_Category_Name',
        'Level3_Category_Name',
        'businessunit',
        'product_gender',
        'product_count']

for i in range(len(cols)):  
    cols.remove(cols[len(cols)-3])
    if len(cols) == 2:
        break
    print(i)
    j_data = generate_counts(products, cols)
    product_counts = pd.merge(product_counts, j_data, on = cols[0:len(cols)-1], how = 'left')

0
1
2


product_counts


from sklearn import preprocessing
product_counts.replace([np.inf, -np.inf], np.nan, inplace=True)
X = product_counts.drop(columns = ['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender']).fillna(0)

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)


from sklearn.decomposition import PCA
pca = PCA()
Xt = pca.fit_transform(X_scaled)


plt.figure(figsize=(16,8))

pd_y = pd.DataFrame(y)
pd_y['label'] = (pd_y['gender'] == 'F')
pd_y['label'] = (pd_y['label']).astype(int)

plot = plt.scatter(Xt[:,0], Xt[:,1], c=pd_y['label'])
plt.legend(handles=plot.legend_elements()[0], labels=['F', 'M'])
plt.show()


pca.explained_variance_ratio_.reshape(8,1).cumsum()

array([0.59788256, 0.72667918, 0.82735275, 0.90219032, 0.93683934,
       0.96557238, 0.9863684 , 1.        ])


pca.explained_variance_ratio_

array([0.59788256, 0.12879662, 0.10067357, 0.07483757, 0.03464902,
       0.02873303, 0.02079602, 0.0136316 ])


from sklearn.manifold import TSNE

# sample 3000 data point because of time concerns
tsne = TSNE(n_components=2, verbose=1, n_iter=1000)
tsne_results = tsne.fit_transform(X_scaled)

[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Indexed 6738 samples in 0.005s...
[t-SNE] Computed neighbors for 6738 samples in 0.708s...
[t-SNE] Computed conditional probabilities for sample 1000 / 6738
[t-SNE] Computed conditional probabilities for sample 2000 / 6738
[t-SNE] Computed conditional probabilities for sample 3000 / 6738
[t-SNE] Computed conditional probabilities for sample 4000 / 6738
[t-SNE] Computed conditional probabilities for sample 5000 / 6738
[t-SNE] Computed conditional probabilities for sample 6000 / 6738
[t-SNE] Computed conditional probabilities for sample 6738 / 6738
[t-SNE] Mean sigma: 0.463619
[t-SNE] KL divergence after 250 iterations with early exaggeration: 76.388504
[t-SNE] KL divergence after 1000 iterations: 1.056580


pd_y = product_counts[['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender']]
pd_y['tsne-2d-one'] = tsne_results[:,0]
pd_y['tsne-2d-two'] = tsne_results[:,1]
plt.figure(figsize=(16,10))
sns.scatterplot(
    x="tsne-2d-one", y="tsne-2d-two",
    hue="product_gender",
    palette=sns.color_palette("hls", 4),
    data=pd_y,
    legend="full",
    alpha=0.3
)

/var/folders/w7/prlcb1cx2rx2jwdcq4ftbmk40000gn/T/ipykernel_14627/232392300.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  pd_y['tsne-2d-one'] = tsne_results[:,0]
/var/folders/w7/prlcb1cx2rx2jwdcq4ftbmk40000gn/T/ipykernel_14627/232392300.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  pd_y['tsne-2d-two'] = tsne_results[:,1]

<AxesSubplot:xlabel='tsne-2d-one', ylabel='tsne-2d-two'>


pd_y


(pd.concat([product_counts, pd_y], axis = 1)).to_csv( "categorical_analysis.csv")


transaction_counts = (train
 .groupby(products.columns.tolist(), dropna = False,
                    as_index=False)
 .agg({"time_stamp": pd.Series.nunique})
)

transaction_counts_by_transaction = (train
 .groupby(products.columns.tolist() + ['user_action'], dropna = False,
                    as_index=False)
 .agg({"time_stamp": pd.Series.nunique})
)


transaction_counts_by_transaction.product_gender = transaction_counts_by_transaction.product_gender.fillna("NotDefined")

transaction_counts_by_transaction = transaction_counts_by_transaction.pivot_table('time_stamp', products.columns.tolist() , 'user_action')
transaction_counts_by_transaction.reset_index( drop=False, inplace=True ) 

transaction_counts_by_transaction = transaction_counts_by_transaction[products.columns.tolist() + ['basket', 'favorite', 'order', 'search', 'visit']]

transaction_counts_by_transaction = transaction_counts_by_transaction.fillna(0)

transaction_counts_by_transaction['total_transaction'] = (transaction_counts_by_transaction['basket'] + 
                                    transaction_counts_by_transaction['favorite'] +
                                    transaction_counts_by_transaction['search'] +
                                    transaction_counts_by_transaction['visit'] +
                                    transaction_counts_by_transaction['order'] )

products_filtered = transaction_counts_by_transaction[transaction_counts_by_transaction['total_transaction'] > 20]


products_filtered.product_gender.value_counts()

Kadın         8646
Unisex        3816
Erkek         1744
NotDefined    1401
Name: product_gender, dtype: int64


product_stats = (train_filtered
 .groupby(['contentid',
           'gender'], dropna = False)
 .agg({"time_stamp": pd.Series.nunique})
 .groupby(['contentid'], dropna = False,
                    as_index=False)
 .apply(lambda x: x / float(x.sum()))
               )
product_stats = product_stats.reset_index().fillna('NotDefined')
product_stats = product_stats[product_stats['gender']=='M'].drop(columns=['gender'])


prod_data = pd.merge(products_filtered, product_stats, on ='contentid', how= 'left')


merged_data = pd.merge(prod_data, product_counts, on =['Level1_Category_Name',
          'Level2_Category_Name',
          'Level3_Category_Name',
          'businessunit',  
          'product_gender'], how= 'left')


merged_data


prod_data.product_gender.value_counts(dropna=False)

Kadın         8646
Unisex        3816
Erkek         1744
NotDefined    1401
Name: product_gender, dtype: int64


from sklearn import preprocessing
merged_data.replace([np.inf, -np.inf], np.nan, inplace=True)
X = merged_data.drop(columns = products.columns.tolist()).fillna(0)

scaler = preprocessing.StandardScaler().fit(X)
X_scaled = scaler.transform(X)


# sample 3000 data point because of time concerns
tsne = TSNE(n_components=2, verbose=1, n_iter=1000)
tsne_results = tsne.fit_transform(X_scaled)

[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Indexed 15607 samples in 0.000s...
[t-SNE] Computed neighbors for 15607 samples in 4.029s...
[t-SNE] Computed conditional probabilities for sample 1000 / 15607
[t-SNE] Computed conditional probabilities for sample 2000 / 15607
[t-SNE] Computed conditional probabilities for sample 3000 / 15607
[t-SNE] Computed conditional probabilities for sample 4000 / 15607
[t-SNE] Computed conditional probabilities for sample 5000 / 15607
[t-SNE] Computed conditional probabilities for sample 6000 / 15607
[t-SNE] Computed conditional probabilities for sample 7000 / 15607
[t-SNE] Computed conditional probabilities for sample 8000 / 15607
[t-SNE] Computed conditional probabilities for sample 9000 / 15607
[t-SNE] Computed conditional probabilities for sample 10000 / 15607
[t-SNE] Computed conditional probabilities for sample 11000 / 15607
[t-SNE] Computed conditional probabilities for sample 12000 / 15607
[t-SNE] Computed conditional probabilities for sample 13000 / 15607
[t-SNE] Computed conditional probabilities for sample 14000 / 15607
[t-SNE] Computed conditional probabilities for sample 15000 / 15607
[t-SNE] Computed conditional probabilities for sample 15607 / 15607
[t-SNE] Mean sigma: 0.472984
[t-SNE] KL divergence after 250 iterations with early exaggeration: 79.714638
[t-SNE] KL divergence after 1000 iterations: 1.381566


pd_y = prod_data[products.columns.tolist()].fillna('NotDefined')
pd_y['tsne-2d-one'] = tsne_results[:,0]
pd_y['tsne-2d-two'] = tsne_results[:,1]
plt.figure(figsize=(16,10))
sns.scatterplot(
    x="tsne-2d-one", y="tsne-2d-two",
    hue="product_gender",
    palette=sns.color_palette("hls", 4),
    data=pd_y,
    legend="full",
    alpha=0.3
)

<AxesSubplot:xlabel='tsne-2d-one', ylabel='tsne-2d-two'>

Kadın     8646
Unisex    3816
Erkek     1744
Name: product_gender, dtype: int64


(pd.concat([prod_data, pd_y], axis = 1)).to_csv( "product_analysis.csv")

	time_stamp	contentid	user_action	sellingprice	product_name	brand_id	brand_name	businessunit	product_gender	category_id	Level1_Category_Id	Level1_Category_Name	Level2_Category_Id	Level2_Category_Name	Level3_Category_Id	Level3_Category_Name	gender	unique_id	type	month	dayOfMonth	dayofWeek	hour	date
5166963	2020-10-14 22:35:35+00:00	35793920.0	search	79.99	Siyah Dantelli Örme Pijama Takımı THMSS20PT0134	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	14	Wednesday	22	2020-10-14
5166935	2020-10-14 22:35:10+00:00	48755368.0	search	68.99	Kadın Beyaz Yıldızlı Pijama Set	972445.0	the DIFFerent	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	14	Wednesday	22	2020-10-14
5166836	2020-10-17 01:42:56+00:00	44297621.0	search	79.99	Bordo Sırt Detaylı Spor Tulum TWOAW21TU0016	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166866	2020-10-17 02:10:52+00:00	37533660.0	search	89.90	Özel Bölgesi Açık Seksi Tasarım Vücut Çorabı	369451.0	Cicişim	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	2	2020-10-17
5166897	2020-10-17 01:45:26+00:00	45798535.0	search	76.99	Kadın Siyah V Yaka Salas Tulum	35127.0	Baroness	Kadın B	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166868	2020-10-17 01:44:00+00:00	46089441.0	search	59.99	Askılı Tulum	970382.0	TİRE MODA	Kadın B	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166869	2020-10-17 01:34:13+00:00	50069275.0	search	99.99	Bordo Örme Pijama Takımı THMAW21PT0509	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166870	2020-10-17 01:39:45+00:00	46690904.0	search	64.99	Siyah Şerit Aksesuar Detaylı Örme Pijama Takım...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166947	2020-10-17 01:37:59+00:00	40422506.0	search	64.99	Pembe Leopar Desenli Crop Bluzlu Pijama Takımı...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166936	2020-10-17 01:41:09.181000+00:00	52514554.0	favorite	118.97	Kadın Siyah Waffle Pijama Takımı	8605.0	Penti	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166878	2020-10-17 01:53:40+00:00	37561306.0	search	69.00	Jartiyer Desenli Özel Bölgesi Açık Fantezi Vüc...	944238.0	dedo	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166880	2020-10-17 01:53:55+00:00	37533660.0	search	89.90	Özel Bölgesi Açık Seksi Tasarım Vücut Çorabı	369451.0	Cicişim	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166865	2020-10-17 01:42:17+00:00	46276203.0	search	159.99	Siyah Bağlama Detaylı Tulum TWOAW21TU0021	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166881	2020-10-17 01:53:20+00:00	34237737.0	search	81.99	Kapüşonlu Vücut Çorabı Bodystocking Mly475	37460.0	Miss Lancy	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166929	2020-10-17 01:42:52+00:00	44297621.0	search	79.99	Bordo Sırt Detaylı Spor Tulum TWOAW21TU0016	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166919	2020-10-17 01:34:47+00:00	50437642.0	search	109.99	Puantiyeli Dokuma Pijama Takımı THMAW21PT0498	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166886	2020-10-17 01:35:50+00:00	42057046.0	search	280.00	Özel Tasarım 6 Lı Siyah Çiçekli Saten Pijama Seti	979706.0	Pembishomewear	İç Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166887	2020-10-17 01:36:05+00:00	50790373.0	search	89.99	Kiremit Örme Pijama Takımı THMAW21PT0514	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166888	2020-10-17 01:35:40+00:00	49218903.0	search	89.99	Gri Kapüşonlu Sloganlı Örme Pijama Takımı THM...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166915	2020-10-17 01:45:02+00:00	47049854.0	search	89.99	Tarçın V Yaka Örme Tulum TWOAW21TU0044	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166914	2020-10-17 01:42:35+00:00	4593208.0	search	119.99	Siyah Kuşaklı Tulum TWOSS19LJ0158	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166913	2020-10-17 01:55:30+00:00	35484989.0	search	59.90	Kadın Siyah Yanları Lazer Kesım Özel Bölgesi A...	944238.0	dedo	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166912	2020-10-17 01:44:23+00:00	42251508.0	search	74.35	Bel Lastikli Salaş Tulum - Gri	40686.0	Mispacoz	Kadın B	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166911	2020-10-17 01:56:40+00:00	47924752.0	search	55.99	Kadın Siyah Saten Tulum Gecelik Takım	969453.0	Rose Alexandra	İç Giyim	Kadın	3110.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166901	2020-10-17 01:37:04+00:00	42057046.0	visit	280.00	Özel Tasarım 6 Lı Siyah Çiçekli Saten Pijama Seti	979706.0	Pembishomewear	İç Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166932	2020-10-17 01:40:19+00:00	39272225.0	search	69.99	Kısa Kollu Dantel Detaylı Viskon Pijama Takım	11994.0	Siyah İnci	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166950	2020-10-17 01:37:27+00:00	50494123.0	search	89.99	Siyah Şerit Detaylı Örme Pijama Takımı THMAW21...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166948	2020-10-17 01:38:55+00:00	38599375.0	search	89.99	Sarı Kolları Fırfırlı Viskon Pijama Takımı THM...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166965	2020-10-17 01:33:51+00:00	35793920.0	search	79.99	Siyah Dantelli Örme Pijama Takımı THMSS20PT0134	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166985	2020-10-17 01:39:11+00:00	38405068.0	visit	79.99	Lila Fırfır Detaylı Örme Pijama Takımı THMSS20...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166983	2020-10-17 01:33:15+00:00	48755368.0	search	68.99	Kadın Beyaz Yıldızlı Pijama Set	972445.0	the DIFFerent	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166977	2020-10-17 01:55:56+00:00	39333639.0	visit	47.20	Dantelli Sırt Dekolteli Büyük Beden Jartiyer T...	944238.0	dedo	Fantezi Giyim	Kadın	3104.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166968	2020-10-17 01:39:41+00:00	50008800.0	search	99.99	Siyah Biyeli Dokuma Pijama Takımı THMAW21PT0292	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166989	2020-10-17 01:41:01+00:00	52514554.0	search	169.95	Kadın Siyah Waffle Pijama Takımı	8605.0	Penti	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166966	2020-10-17 01:33:39+00:00	32894924.0	search	99.99	Pudra Örme Pijama Takımı THMAW20PT0265	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166837	2020-10-17 01:43:07+00:00	44297536.0	visit	79.99	Siyah Sırt Detaylı Spor Tulum TWOAW21TU0017	40.0	TRENDYOLMİLLA	PL Woman	Kadın	546.0	522.0	Giyim	1192.0	Salopet & Tulum	546.0	Tulum	M	3991	train	10	17	Saturday	1	2020-10-17
5166841	2020-10-17 01:37:49+00:00	37995791.0	search	144.90	Dört Mevsimlik Uzun Kollu Gömlek Yaka Pijama T...	955522.0	Pijama Denizi	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166959	2020-10-17 01:53:28+00:00	35484992.0	search	58.90	Kadın Siyah Fiyonklu Sırt Detaylı Fantazi Vücu...	944238.0	dedo	Fantezi Giyim	Kadın	3102.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	17	Saturday	1	2020-10-17
5166859	2020-10-17 01:35:02+00:00	36688389.0	search	122.00	Saten 3 Lü Pijama Sabahlık Takım	25005.0	MOONGIRL	İç Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166964	2020-10-17 01:35:14+00:00	47467139.0	search	129.99	Ekru Bağlama Detaylı Saten Pijama Takımı THMA...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166856	2020-10-17 01:35:29+00:00	35036201.0	search	69.99	Gül Kurusu Bağlama Detaylı Örme Pijama Takımı ...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166854	2020-10-17 01:40:06+00:00	38922050.0	search	69.99	Çok Renkli Ekose Viskon Şortlu Pijama Takımı T...	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166857	2020-10-17 01:36:41+00:00	42057047.0	search	282.80	Özel Tasarım 6 Lı Somon Çiçekli Saten Pijama Seti	979706.0	Pembishomewear	İç Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166858	2020-10-17 01:36:51+00:00	42057026.0	visit	210.00	Biyeli Pink 5 Li Saten Pijama Seti	979706.0	Pembishomewear	İç Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	17	Saturday	1	2020-10-17
5166942	2020-10-20 10:34:37+00:00	50911391.0	search	54.99	Kız Çocuk Beyaz Peluş Uzun Kol Bolero	958058.0	Lumia Baby	Kids & Baby Fashion	Kadın	537.0	522.0	Giyim	2867.0	Dış Giyim	537.0	Panço	M	3991	train	10	20	Tuesday	10	2020-10-20
5166941	2020-10-20 14:04:16+00:00	32055411.0	visit	12474.00	iPhone 11 Pro Max 64GB Uzay Grisi Cep Telefonu...	1617.0	Apple	Cep Telefonu & Aksesuarları	Unisex	3719.0	1071.0	Elektronik	1054.0	Telefon	3719.0	Akıllı Cep Telefonu	M	3991	train	10	20	Tuesday	14	2020-10-20
5166917	2020-10-20 10:34:58+00:00	50911391.0	search	54.99	Kız Çocuk Beyaz Peluş Uzun Kol Bolero	958058.0	Lumia Baby	Kids & Baby Fashion	Kadın	537.0	522.0	Giyim	2867.0	Dış Giyim	537.0	Panço	M	3991	train	10	20	Tuesday	10	2020-10-20
5166853	2020-10-20 10:32:55+00:00	43486328.0	visit	0.00	Kız Çocuk Beyaz Kürk Mont	969118.0	BY MOMY	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	10	20	Tuesday	10	2020-10-20
5166851	2020-10-20 10:33:30+00:00	33038054.0	search	137.99	Peluş Mont	289.0	Panço	Kids & Baby Fashion	Kadın	534.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	10	20	Tuesday	10	2020-10-20
5166898	2020-10-21 00:44:20+00:00	51888331.0	search	58.00	Kız Çocuk Beyaz Pelüş Bolero	958058.0	Lumia Baby	Kids & Baby Fashion	Kadın	606.0	522.0	Giyim	2867.0	Dış Giyim	1181.0	Ceket & Yelek	M	3991	train	10	21	Wednesday	0	2020-10-21
5166979	2020-10-21 00:43:18+00:00	53552554.0	search	69.99	Kız Çocuk Pembe Peluş Kaban	990495.0	ÜN-KO	Kids & Baby Fashion	Unisex	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	10	21	Wednesday	0	2020-10-21
5166937	2020-10-22 01:26:39+00:00	7085072.0	search	127.68	50 PLUS 60 KAPSÜL	20716.0	Pharmaton	Sağlık	Unisex	2322.0	1219.0	Süpermarket	2377.0	Sağlık	2320.0	Gıda Takviyesi & Vitamin	M	3991	train	10	22	Thursday	1	2020-10-22
5166879	2020-10-23 01:37:37+00:00	37673601.0	visit	119.99	Kadın Siyah Kruvaze Yaka Beli Kuşaklı Tulum	952588.0	BÜYÜK BEDEN BESA	Büyük Beden	Kadın	2981.0	522.0	Giyim	2961.0	Büyük Beden	2981.0	Büyük Beden Tulum	M	3991	train	10	23	Friday	1	2020-10-23
5166893	2020-10-24 14:11:57+00:00	48726289.0	visit	29.99	Kız Bebek Antrasit Melanj L8N T-Shirt	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	10	24	Saturday	14	2020-10-24
5166842	2020-10-26 14:22:05+00:00	32105585.0	visit	392.00	Erkek 5 Parca Damat Çeyiz Pijama Takimi 5560	121.0	Pierre Cardin	İç Giyim	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	26	Monday	14	2020-10-26
5166988	2020-10-27 13:48:58+00:00	37604266.0	search	185.00	V Yaka Pijama Tk.	1471.0	D'S Damat	Erkek A	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166949	2020-10-27 21:49:10+00:00	51479282.0	search	142.99	Kadın Ekru Pelüş ve Nakış Detaylı Göz Bandı He...	333748.0	ARCAN	Büyük Beden	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	21	2020-10-27
5166986	2020-10-27 13:50:32+00:00	37657886.0	search	273.00	U.s.polo Assn 12000 5'li Yeni Sezon Penye Dama...	13054.0	U.S POLO	Ev Giyim	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166933	2020-10-27 13:51:10+00:00	3621131.0	visit	0.00	Erkek Lacivert Uzun Kollu Pijama Takımı	121.0	Pierre Cardin	Ev Giyim	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166955	2020-10-27 13:51:48+00:00	34743054.0	visit	149.99	Deri Çeyiz Terliği	1471.0	D'S Damat	Branded Shoes B	Erkek	958.0	403.0	Ayakkabı	1170.0	Ev Terliği & Botu	958.0	Ev Terliği	M	3991	train	10	27	Tuesday	13	2020-10-27
5166945	2020-10-27 21:49:06+00:00	37995791.0	search	144.90	Dört Mevsimlik Uzun Kollu Gömlek Yaka Pijama T...	955522.0	Pijama Denizi	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	21	2020-10-27
5166987	2020-10-27 21:50:59+00:00	47995563.0	search	114.90	Welsoft Polar Kız Çocuk Tulum 4538	13890.0	Akbeniz	Kadın B	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	21	2020-10-27
5166967	2020-10-27 13:50:18+00:00	37603622.0	search	285.00	Erkek Pijama Takımı Düğmeli Comfort	1471.0	D'S Damat	Erkek A	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166918	2020-10-27 13:51:39+00:00	42193425.0	visit	360.00	U.s.polo Assn 12000 5'li Yeni Sezon Erkek Peny...	19.0	U.S. Polo Assn.	Ev Giyim	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166890	2020-10-27 13:14:19+00:00	36410137.0	search	439.00	Robdöşambr 5540 Damatlık 5 Li Takım Pijama Set	121.0	Pierre Cardin	Ev Giyim	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166891	2020-10-27 21:49:44+00:00	53579534.0	search	79.99	Kadın Gri Pijama Takım	7651.0	LC Waikiki	Kadın B	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	21	2020-10-27
5166855	2020-10-27 13:52:32+00:00	43880237.0	search	339.00	Us Polo 12003 5 Li Erkek Çeyizlik Ropteşambır Set	19.0	U.S. Polo Assn.	Ev Giyim	Unisex	2358.0	522.0	Giyim	2870.0	Ev Giyim	2358.0	Sabahlık	M	3991	train	10	27	Tuesday	13	2020-10-27
5166882	2020-10-27 13:46:54+00:00	32739867.0	search	159.99	Pembe Kız Çocuk Pelus Mont	13758.0	Koton Kids	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	10	27	Tuesday	13	2020-10-27
5166889	2020-10-27 13:49:39+00:00	37603583.0	visit	169.00	V Yaka Kısakol Pijama Tk.	1471.0	D'S Damat	Erkek A	Erkek	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	27	Tuesday	13	2020-10-27
5166896	2020-10-27 21:52:52+00:00	2784098.0	visit	109.00	Ruj - Lipstick Velvet Teddy 3 g 773602077540	7821.0	M.A.C	Makyaj	Kadın	644.0	1070.0	Kozmetik & Kişisel Bakım	641.0	Makyaj	642.0	Dudak Makyajı	M	3991	train	10	27	Tuesday	21	2020-10-27
5166927	2020-10-28 22:16:43+00:00	48592797.0	visit	215.00	Gold Hello Kity Çocuk Valizi	2352.0	Laguna	Çanta & Valiz	Kadın	450.0	368.0	Aksesuar	435.0	Çanta	450.0	Valiz & Bavul	M	3991	train	10	28	Wednesday	22	2020-10-28
5166926	2020-10-28 20:40:09+00:00	48592797.0	visit	215.00	Gold Hello Kity Çocuk Valizi	2352.0	Laguna	Çanta & Valiz	Kadın	450.0	368.0	Aksesuar	435.0	Çanta	450.0	Valiz & Bavul	M	3991	train	10	28	Wednesday	20	2020-10-28
5166953	2020-10-28 22:17:18+00:00	38510840.0	search	258.57	The Collagen Beauty 30 Tüp X 40 Ml 1000200009044	41418.0	DAY2DAY	Sağlık	NaN	3958.0	1219.0	Süpermarket	2377.0	Sağlık	2320.0	Gıda Takviyesi & Vitamin	M	3991	train	10	28	Wednesday	22	2020-10-28
5166916	2020-10-28 22:28:29+00:00	44596282.0	search	17.99	Erkek Çocuk Yeni Siyah Cvl Atlet	7651.0	LC Waikiki	Kids & Baby Fashion	Erkek	603.0	522.0	Giyim	549.0	İç Giyim	603.0	Atlet	M	3991	train	10	28	Wednesday	22	2020-10-28
5166894	2020-10-28 22:17:58+00:00	38511763.0	search	291.20	The Collagen Beauty Intense (30 Saşe)	41418.0	DAY2DAY	Sağlık	NaN	3958.0	1219.0	Süpermarket	2377.0	Sağlık	2320.0	Gıda Takviyesi & Vitamin	M	3991	train	10	28	Wednesday	22	2020-10-28
5166844	2020-10-28 22:15:18+00:00	38510840.0	search	258.57	The Collagen Beauty 30 Tüp X 40 Ml 1000200009044	41418.0	DAY2DAY	Sağlık	NaN	3958.0	1219.0	Süpermarket	2377.0	Sağlık	2320.0	Gıda Takviyesi & Vitamin	M	3991	train	10	28	Wednesday	22	2020-10-28
5166952	2020-10-28 22:15:05+00:00	43454994.0	search	270.17	Day2day The Beauty 30 Tüp X 40 Ml Kolojen	969907.0	Collagen	Sağlık	NaN	3958.0	1219.0	Süpermarket	2377.0	Sağlık	2320.0	Gıda Takviyesi & Vitamin	M	3991	train	10	28	Wednesday	22	2020-10-28
5166991	2020-10-29 10:43:49+00:00	32796364.0	visit	79.99	Bej Omzu Açık Askılı Triko Kazak TWOAW20KZ0911	40.0	TRENDYOLMİLLA	PL Woman	Kadın	599.0	522.0	Giyim	2871.0	Üst Giyim	1188.0	Kazak & Hırka	M	3991	train	10	29	Thursday	10	2020-10-29
5166921	2020-10-29 10:44:01.543000+00:00	32796364.0	favorite	79.99	Bej Omzu Açık Askılı Triko Kazak TWOAW20KZ0911	40.0	TRENDYOLMİLLA	PL Woman	Kadın	599.0	522.0	Giyim	2871.0	Üst Giyim	1188.0	Kazak & Hırka	M	3991	train	10	29	Thursday	10	2020-10-29
5166920	2020-10-29 20:12:44+00:00	49479489.0	search	69.99	Kadın Renkli Digital Baskı Kapşonlu Salaş Swea...	968879.0	caddecity	Kadın B	Kadın	601.0	522.0	Giyim	2871.0	Üst Giyim	601.0	Sweatshirt	M	3991	train	10	29	Thursday	20	2020-10-29
5166884	2020-10-29 23:16:47+00:00	36307043.0	visit	29.95	Kadın Siyah Desenli Tayt TYT1006 - H5 ADX-0000...	1042.0	Addax	Kadın B	Kadın	531.0	522.0	Giyim	2869.0	Alt Giyim	531.0	Tayt	M	3991	train	10	29	Thursday	23	2020-10-29
5166944	2020-10-29 23:16:11+00:00	44277793.0	search	39.99	Kahverengi Yüksek Bel Basic Örme Tayt TWOAW20T...	40.0	TRENDYOLMİLLA	PL Woman	Kadın	531.0	522.0	Giyim	2869.0	Alt Giyim	531.0	Tayt	M	3991	train	10	29	Thursday	23	2020-10-29
5166931	2020-10-29 23:16:31+00:00	44445080.0	search	29.95	Kadın Siyah Yüksek Bel Tayt	1042.0	Addax	Kadın B	Kadın	531.0	522.0	Giyim	2869.0	Alt Giyim	531.0	Tayt	M	3991	train	10	29	Thursday	23	2020-10-29
5166871	2020-10-29 10:44:38+00:00	46277050.0	visit	89.99	Taş Askı Detaylı Renk Bloklu Triko Kazak TWOAW...	40.0	TRENDYOLMİLLA	PL Woman	Kadın	599.0	522.0	Giyim	2871.0	Üst Giyim	1188.0	Kazak & Hırka	M	3991	train	10	29	Thursday	10	2020-10-29
5166867	2020-10-29 19:52:20+00:00	39333639.0	visit	69.90	Dantelli Sırt Dekolteli Büyük Beden Jartiyer T...	944238.0	dedo	Fantezi Giyim	Kadın	3104.0	522.0	Giyim	3095.0	Fantezi Giyim	3096.0	Fantezi İç Giyim	M	3991	train	10	29	Thursday	19	2020-10-29
5166849	2020-10-29 15:43:34+00:00	35725176.0	visit	370.00	Naomi Siyah Süet Taşlı Stiletto	42297.0	Mio Gusto	Branded Shoes B	Kadın	431.0	403.0	Ayakkabı	430.0	Topuklu Ayakkabı	431.0	Abiye Ayakkabı	M	3991	train	10	29	Thursday	15	2020-10-29
5166846	2020-10-29 23:18:01+00:00	43731819.0	search	59.00	Kadın Beyaz Vatkalı T-shirt Elbise	953900.0	Laluvia	Kadın B	Kadın	1182.0	522.0	Giyim	1182.0	Elbise	1182.0	Elbise	M	3991	train	10	29	Thursday	23	2020-10-29
5166951	2020-10-29 23:17:32+00:00	44279523.0	search	59.90	Kadın Siyah Vatkalı Bağlamalı Crop Örme T-shirt	960163.0	Naz Butik	Kadın B	Unisex	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	10	29	Thursday	23	2020-10-29
5166843	2020-10-29 23:17:18+00:00	43404433.0	search	39.99	Kadın Beyaz Bisiklet Yaka Omuzu Vatkalı Kısa K...	950382.0	Park Karon	Kadın B	Unisex	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	10	29	Thursday	23	2020-10-29
5166934	2020-10-30 23:56:10+00:00	47957713.0	search	97.90	Kadın Somon Kalpli Penye Uzun Kol Pijama Takım	980090.0	LİKAPA	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	30	Friday	23	2020-10-30
5166861	2020-10-30 23:55:05+00:00	51725314.0	visit	53.99	Kız Çocuk Açık Kahverengi Gxt Hırka	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	598.0	522.0	Giyim	2871.0	Üst Giyim	1188.0	Kazak & Hırka	M	3991	train	10	30	Friday	23	2020-10-30
5166840	2020-10-30 23:56:33+00:00	46263977.0	visit	109.99	Tavşan Desenli Dokuma Pijama Takımı THMAW21PT0048	40.0	TRENDYOLMİLLA	PL Homewear	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	10	30	Friday	23	2020-10-30
5166939	2020-11-02 01:34:08+00:00	2205703.0	search	69.00	Beyaz Kız Çocuk Babet 90461	5019.0	Frozen	Kids & Baby Fashion	Kadın	410.0	403.0	Ayakkabı	1171.0	Günlük Ayakkabı	410.0	Babet	M	3991	train	11	2	Monday	1	2020-11-02
5166892	2020-11-03 23:28:38+00:00	48726289.0	visit	29.99	Kız Bebek Antrasit Melanj L8N T-Shirt	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	11	3	Tuesday	23	2020-11-03
5166848	2020-11-03 23:30:41+00:00	51285342.0	visit	109.90	Black Passion 50 ml Edp Erkek Parfüm Dnep21001	42558.0	Duke Nickle	Saat B	Erkek	661.0	1070.0	Kozmetik & Kişisel Bakım	661.0	Parfüm	661.0	Parfüm	M	3991	train	11	3	Tuesday	23	2020-11-03
5166990	2020-11-08 02:46:31+00:00	48726289.0	visit	29.99	Kız Bebek Antrasit Melanj L8N T-Shirt	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	11	8	Sunday	2	2020-11-08
5166839	2020-11-10 02:42:35+00:00	35695919.0	visit	129.99	Skinny Jean Pantolon 67002517	13907.0	MANGO Kids	Kids & Baby Fashion	Kadın	1186.0	522.0	Giyim	2869.0	Alt Giyim	1186.0	Jeans	M	3991	train	11	10	Tuesday	2	2020-11-10
5166981	2020-11-10 00:47:06+00:00	46376576.0	search	282.83	Kız Çocuk Suni Kürklü Kapüşonlu Şişme Mont	952658.0	Le Ville	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	10	Tuesday	0	2020-11-10
5166961	2020-11-10 02:45:00+00:00	51613548.0	search	139.99	Kız Çocuk Haki Cs5 Mont	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	10	Tuesday	2	2020-11-10
5166982	2020-11-10 02:55:46+00:00	32792287.0	search	119.99	Erkek Jean CL1046673	110.0	Colin's	GAS Denim	Erkek	1186.0	522.0	Giyim	2869.0	Alt Giyim	1186.0	Jeans	M	3991	train	11	10	Tuesday	2	2020-11-10
5166980	2020-11-10 00:49:17+00:00	50585504.0	search	99.99	Kız Çocuk Kapüşonlu Mont	3268.0	DeFacto	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	10	Tuesday	0	2020-11-10
5166957	2020-11-10 03:18:14+00:00	40385400.0	visit	264.00	Bt Erkek Ayakkabı Siyah Ch257	16348.0	Chekich	Branded Shoes B	Erkek	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	10	Tuesday	3	2020-11-10
5166850	2020-11-10 00:49:26+00:00	31909100.0	search	229.99	Kız Çocuk Kapüşonlu Fermuarlı Şişme Uzun Mont	3268.0	DeFacto	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	10	Tuesday	0	2020-11-10
5166883	2020-11-10 02:55:06+00:00	3001878.0	search	69.95	Erkek Pantolon CL1017495	110.0	Colin's	GAS Denim	Erkek	529.0	522.0	Giyim	2869.0	Alt Giyim	529.0	Pantolon	M	3991	train	11	10	Tuesday	2	2020-11-10
5166998	2020-11-10 03:11:11+00:00	49980118.0	visit	199.00	Ayakkabı Court Siyah	481.0	Kappa	Sportswear	Erkek	429.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	429.0	Yürüyüş Ayakkabısı	M	3991	train	11	10	Tuesday	3	2020-11-10
5166838	2020-11-10 00:48:11+00:00	31909100.0	search	229.99	Kız Çocuk Kapüşonlu Fermuarlı Şişme Uzun Mont	3268.0	DeFacto	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	10	Tuesday	0	2020-11-10
5166895	2020-11-10 03:12:04+00:00	4016730.0	visit	79.90	Erkek Yeşil Dijital Basklı T-shirt	481.0	Kappa	Sportswear	Erkek	3214.0	3186.0	Spor & Outdoor	3187.0	Spor Giyim	3214.0	Spor T-Shirt	M	3991	train	11	10	Tuesday	3	2020-11-10
5166962	2020-11-10 02:55:54+00:00	34095422.0	search	99.95	Erkek Jean CL1048659	110.0	Colin's	GAS Denim	Erkek	1186.0	522.0	Giyim	2869.0	Alt Giyim	1186.0	Jeans	M	3991	train	11	10	Tuesday	2	2020-11-10
5166905	2020-11-10 02:49:42+00:00	40218783.0	visit	222.58	Siyah Beyaz Erkek Casual 0012211	93397.0	Riccon	Branded Shoes B	Erkek	411.0	403.0	Ayakkabı	1171.0	Günlük Ayakkabı	411.0	Casual Ayakkabı	M	3991	train	11	10	Tuesday	2	2020-11-10
5166907	2020-11-10 03:18:19+00:00	43370562.0	visit	689.00	Erkek Siyah Skech Air Ultra Flex Orburn Ayakk...	8616.0	SKECHERS	Sportswear	Unisex	429.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	429.0	Yürüyüş Ayakkabısı	M	3991	train	11	10	Tuesday	3	2020-11-10
5166956	2020-11-12 01:17:13+00:00	41476219.0	search	160.90	24715	1410.0	Jump	Branded Shoes B	Erkek	429.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	429.0	Yürüyüş Ayakkabısı	M	3991	train	11	12	Thursday	1	2020-11-12
5166924	2020-11-12 01:15:40+00:00	42641175.0	search	149.90	Erkek Siyah Spor Ayakkabı 4439	21543.0	Çetintaş	Branded Shoes B	Erkek	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	12	Thursday	1	2020-11-12
5166997	2020-11-12 01:16:29+00:00	31651543.0	search	289.00	CH257 Taba BT Erkek Günlük Ayakkabı	16348.0	Chekich	Branded Shoes B	Erkek	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	12	Thursday	1	2020-11-12
5166978	2020-11-12 13:58:19+00:00	48042309.0	visit	84.95	Lacivert Çıtçıtlı Dantelli Transparan Sırt Çap...	23275.0	Cottonhill	İç Giyim	Kadın	552.0	522.0	Giyim	549.0	İç Giyim	552.0	Babydoll	M	3991	train	11	12	Thursday	13	2020-11-12
5166960	2020-11-14 23:23:47+00:00	50415789.0	search	149.99	Kız Bebek Mat Pembe Baskıl Lt3 Mont	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	14	Saturday	23	2020-11-14
5166900	2020-11-14 23:23:54+00:00	4824508.0	visit	119.99	Pembe Kız Bebek Kapüsonlu Kaban	13758.0	Koton Kids	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	14	Saturday	23	2020-11-14
5166852	2020-11-14 23:21:51+00:00	41914064.0	search	129.99	Kız Bebek Siyah Baskılı Lqj Mont	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	14	Saturday	23	2020-11-14
5166943	2020-11-14 23:21:37+00:00	35298852.0	search	149.99	Kız Çocuk Kapitoneli Puffer Mont	3268.0	DeFacto	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	14	Saturday	23	2020-11-14
5166930	2020-11-14 23:21:12+00:00	51563016.0	search	245.00	Kız Çocuk Siyah Kedicik Şişme Tak Çıkar Kürklü...	980288.0	KOKOŞ BEBEK	Kids & Baby Fashion	Kadın	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	14	Saturday	23	2020-11-14
5166909	2020-11-15 17:38:21+00:00	47984013.0	search	109.00	Kurdela 100x150 Bi99 Çocuk Odası Halısı	40593.0	Bebişim Halı	Halı/Kilim	Unisex	2848.0	758.0	Ev & Mobilya	476.0	Ev Tekstili	484.0	Halı & Kilim & Paspas	M	3991	train	11	15	Sunday	17	2020-11-15
5166974	2020-11-17 00:07:01+00:00	31353394.0	search	59.90	Pastel Kalpler Çocuk Odası Duvar Sticker CS-802	31203.0	Dekor Loft	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166975	2020-11-17 00:09:30+00:00	38666171.0	search	39.00	Çocuk Bebek Odası Duvar Sticker Sarı Yıldızlar...	130947.0	ArtPoint	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166877	2020-11-17 00:05:40+00:00	39138829.0	search	37.98	Balonlu Tavşan Boy Ölçer Duvar Sticker Seti	337349.0	Sim Tasarım	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166976	2020-11-17 00:06:10+00:00	39213372.0	search	28.47	Pembe Balerin Duvar Sticker Seti	337349.0	Sim Tasarım	Ev Dekorasyon	Kadın	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166847	2020-11-17 00:04:17+00:00	4530917.0	search	115.37	Kınder Sarkıt Pembe	14652.0	Modelight	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166946	2020-11-17 01:11:13+00:00	53565735.0	visit	69.99	Kadın Siyah Pijama Takım	7651.0	LC Waikiki	Kadın A	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	11	17	Tuesday	1	2020-11-17
5166876	2020-11-17 00:07:59+00:00	34050625.0	search	58.70	Balerin Ve Beyaz Kuğu Çocuk Odası Sticker Cs-845	31203.0	Dekor Loft	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166973	2020-11-17 00:09:45+00:00	2429388.0	search	45.90	48 Adet Altın Renkli Kalpler Çocuk ve Bebek Od...	20253.0	Tilki Dünyası	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166992	2020-11-17 00:05:13+00:00	52238025.0	search	59.00	Çocuk Odası Ahşap Ledli Gece Lambası Avize	995345.0	MCT	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166993	2020-11-17 00:04:43+00:00	33057448.0	search	69.90	Beyaz Pudra Ponpon Çocuk Genç Bebek Odası Aviz...	112197.0	Egelight	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166994	2020-11-17 00:04:14+00:00	3466197.0	search	64.90	Kelebek 3D Tekli Sarkıt Avize Pembe	953433.0	HOMİNG	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166908	2020-11-17 00:09:05+00:00	33248547.0	search	79.90	100 Adet Fosforlu Yıldız Ay Gezegen Gökyüzü Ka...	40955.0	Bundera	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	0	2020-11-17
5166906	2020-11-17 00:04:57+00:00	36293138.0	search	160.00	Çocuk Odası Pembe Tüylü Avize Abajur Seti Beya...	41202.0	TEK-AY	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166995	2020-11-17 00:04:34+00:00	31769678.0	search	64.98	Pasta Tekli Sarkıt Avize AYD-1894	953433.0	HOMİNG	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166958	2020-11-17 01:10:42+00:00	2429388.0	visit	45.90	48 Adet Altın Renkli Kalpler Çocuk ve Bebek Od...	20253.0	Tilki Dünyası	Ev Dekorasyon	Unisex	3465.0	758.0	Ev & Mobilya	458.0	Ev Dekorasyon	3465.0	Dekoratif Sticker	M	3991	train	11	17	Tuesday	1	2020-11-17
5166971	2020-11-17 00:03:37+00:00	6280483.0	search	59.90	Peri Tekli Pembe Kalpli Avize	14619.0	Begüsa	Aydınlatma	NaN	934.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	934.0	Avize	M	3991	train	11	17	Tuesday	0	2020-11-17
5166863	2020-11-18 22:02:16+00:00	6114624.0	search	184.60	Serpent Abajur Siyah	16968.0	Mudulight	Aydınlatma	NaN	931.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	931.0	Abajur	M	3991	train	11	18	Wednesday	22	2020-11-18
5166864	2020-11-18 22:03:01+00:00	46889739.0	search	74.90	Fiesta Sun Metal Ayaklı Abajur Masa Lambası - ...	976765.0	Bysay Fiesta	Aydınlatma	NaN	931.0	758.0	Ev & Mobilya	930.0	Elektrik & Aydınlatma	931.0	Abajur	M	3991	train	11	18	Wednesday	22	2020-11-18
5166902	2020-11-19 01:21:55+00:00	53565735.0	visit	69.99	Kadın Siyah Pijama Takım	7651.0	LC Waikiki	Kadın A	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	11	19	Thursday	1	2020-11-19
5166972	2020-11-22 01:12:53+00:00	39494329.0	search	18.95	Hızlı Yapıştırıcı 200 Ml + 50 Gr	37275.0	Selsil	Kırtasiye & Ofis	Unisex	1524.0	2859.0	Yaşam	1216.0	Kırtasiye & Ofis Malzemeleri	2902.0	Masaüstü Gereçleri	M	3991	train	11	22	Sunday	1	2020-11-22
5166885	2020-11-23 02:06:36+00:00	55301947.0	search	139.90	Dört Mevsimlik Uzun Kollu Düğmeli Gömlek Yaka ...	955522.0	Pijama Denizi	Ev Giyim	Kadın	563.0	522.0	Giyim	2870.0	Ev Giyim	561.0	Pijama	M	3991	train	11	23	Monday	2	2020-11-23
5166984	2020-11-23 02:08:22+00:00	50293102.0	search	44.90	Kırmızı Seksi Büyük Beden Fantazi Gecelik	991134.0	Tarzınbu	Ev Giyim	Kadın	553.0	522.0	Giyim	2870.0	Ev Giyim	553.0	Gecelik	M	3991	train	11	23	Monday	2	2020-11-23
5166970	2020-11-25 00:49:58+00:00	31131257.0	search	171.42	Hakiki Deri Siyah Erkek Bot	2018.0	Dockers By Gerli	Branded Shoes A	Erkek	411.0	403.0	Ayakkabı	1171.0	Günlük Ayakkabı	411.0	Casual Ayakkabı	M	3991	train	11	25	Wednesday	0	2020-11-25
5167000	2020-11-25 00:45:30+00:00	44823342.0	visit	119.80	Three Cactus Djt 2 Li Set Banyo Paspası	23248.0	Chilai Home	Banyo	NaN	2993.0	758.0	Ev & Mobilya	452.0	Banyo	2993.0	Banyo Paspası	M	3991	train	11	25	Wednesday	0	2020-11-25
5166862	2020-11-25 00:51:57+00:00	42055063.0	search	229.90	Tom Jones Kol Saati+bileklik Hediye	647.0	Daniel Klein	Saat B	Kadın	387.0	368.0	Aksesuar	387.0	Saat	387.0	Saat	M	3991	train	11	25	Wednesday	0	2020-11-25
5166873	2020-11-27 21:34:16+00:00	33322960.0	basket	36.90	Ayçiçek Yağı 2 Lt	23907.0	Yudum	Gıda ve İçecek	NaN	1439.0	1219.0	Süpermarket	1385.0	Gıda & İçecek	2448.0	Yağ ve Sos	M	3991	train	11	27	Friday	21	2020-11-27
5166874	2020-11-27 21:33:40+00:00	33322960.0	search	36.90	Ayçiçek Yağı 2 Lt	23907.0	Yudum	Gıda ve İçecek	NaN	1439.0	1219.0	Süpermarket	1385.0	Gıda & İçecek	2448.0	Yağ ve Sos	M	3991	train	11	27	Friday	21	2020-11-27
5166845	2020-11-27 21:40:54+00:00	58312103.0	visit	185.00	Easy Wet Wipes Hijyenik Islak Kova Mendil 500 ...	982094.0	VEBOX	Ev Bakım ve Temizlik	Unisex	1423.0	1219.0	Süpermarket	1382.0	Ev Bakım ve Temizlik	1389.0	Kağıt Ürünleri	M	3991	train	11	27	Friday	21	2020-11-27
5166954	2020-11-27 21:40:30+00:00	58312103.0	visit	185.00	Easy Wet Wipes Hijyenik Islak Kova Mendil 500 ...	982094.0	VEBOX	Ev Bakım ve Temizlik	Unisex	1423.0	1219.0	Süpermarket	1382.0	Ev Bakım ve Temizlik	1389.0	Kağıt Ürünleri	M	3991	train	11	27	Friday	21	2020-11-27
5166969	2020-11-27 21:41:02+00:00	46829174.0	visit	135.00	Easy Wet Wipes Islak Kova Mendil	982094.0	VEBOX	Ev Bakım ve Temizlik	Unisex	1423.0	1219.0	Süpermarket	1382.0	Ev Bakım ve Temizlik	1389.0	Kağıt Ürünleri	M	3991	train	11	27	Friday	21	2020-11-27
5166875	2020-11-27 21:35:39+00:00	5985876.0	basket	249.00	Metalix Midi Full Metal Çaydanlık	15004.0	Jumbo	Sofra & Mutfak	Unisex	2424.0	758.0	Ev & Mobilya	896.0	Sofra & Mutfak	909.0	Pişirme	M	3991	train	11	27	Friday	21	2020-11-27
5166903	2020-11-27 21:40:22+00:00	46829174.0	visit	135.00	Easy Wet Wipes Islak Kova Mendil	982094.0	VEBOX	Ev Bakım ve Temizlik	Unisex	1423.0	1219.0	Süpermarket	1382.0	Ev Bakım ve Temizlik	1389.0	Kağıt Ürünleri	M	3991	train	11	27	Friday	21	2020-11-27
5166925	2020-11-27 21:37:10+00:00	51500565.0	visit	919.00	Kadın Sneaker - Lifestyle - MT574ATD	865.0	New Balance	Sportswear	Kadın	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	27	Friday	21	2020-11-27
5166996	2020-11-27 21:32:20+00:00	51500565.0	search	919.00	Kadın Sneaker - Lifestyle - MT574ATD	865.0	New Balance	Sportswear	Kadın	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	27	Friday	21	2020-11-27
5166904	2020-11-27 21:35:25+00:00	5985876.0	search	249.00	Metalix Midi Full Metal Çaydanlık	15004.0	Jumbo	Sofra & Mutfak	Unisex	2424.0	758.0	Ev & Mobilya	896.0	Sofra & Mutfak	909.0	Pişirme	M	3991	train	11	27	Friday	21	2020-11-27
5166999	2020-11-27 21:37:05+00:00	51500581.0	search	835.05	Erkek Sneaker - Lifestyle - CM997HFM	865.0	New Balance	Sportswear	Kadın	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	27	Friday	21	2020-11-27
5166923	2020-11-28 00:31:25+00:00	40403195.0	search	283.39	St Erkek Ayakkabı Siyah Ch253	16348.0	Chekich	Branded Shoes B	Erkek	975.0	403.0	Ayakkabı	420.0	Spor Ayakkabı	975.0	Sneaker	M	3991	train	11	28	Saturday	0	2020-11-28
5166910	2020-11-28 01:24:52+00:00	48326603.0	search	165.00	Yuvarlak Orta Sehpa Atlantik Çam Dergilik Gaze...	943633.0	Akkaya Ticaret	Salon &Mutfak Mobilya	NaN	2113.0	758.0	Ev & Mobilya	503.0	Mobilya	2087.0	Salon & Oturma Odası	M	3991	train	11	28	Saturday	1	2020-11-28
5166938	2020-11-28 00:29:06+00:00	38562035.0	search	387.00	Apricot Bio Granit Mega Black 9 Parça Tencere ...	852.0	Karaca	Sofra & Mutfak	Unisex	912.0	758.0	Ev & Mobilya	896.0	Sofra & Mutfak	909.0	Pişirme	M	3991	train	11	28	Saturday	0	2020-11-28
5166922	2020-11-28 01:32:20+00:00	35297420.0	search	59.99	Kız Bebek Koyu Turuncu Gmt Alt-Üst Takım	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	2160.0	522.0	Giyim	584.0	Takımlar	2160.0	Alt-Üst Takım	M	3991	train	11	28	Saturday	1	2020-11-28
5166899	2020-11-28 00:28:21+00:00	46374301.0	search	424.75	Erkek Siyah Kapüşonlu Mont	159297.0	DENSMOOD	Erkek B	Erkek	535.0	522.0	Giyim	2867.0	Dış Giyim	1187.0	Kaban & Mont	M	3991	train	11	28	Saturday	0	2020-11-28
5166928	2020-11-28 01:25:21+00:00	31092053.0	search	88.90	Zigon Sehpa 3 Lü Ahşap Torna Ayak Ceviz	36558.0	Aldın Aldın	Salon &Mutfak Mobilya	Unisex	2115.0	758.0	Ev & Mobilya	503.0	Mobilya	2087.0	Salon & Oturma Odası	M	3991	train	11	28	Saturday	1	2020-11-28
5166940	2020-11-28 01:25:01+00:00	64843505.0	search	72.00	3 Lü Zigon Sehpa	974874.0	MSMOBİLYA	Salon &Mutfak Mobilya	NaN	2115.0	758.0	Ev & Mobilya	503.0	Mobilya	2087.0	Salon & Oturma Odası	M	3991	train	11	28	Saturday	1	2020-11-28
5166860	2020-11-28 01:30:49+00:00	62833047.0	visit	29.99	Kız Bebek Gülkurusu H2H T-Shirt	7651.0	LC Waikiki	Kids & Baby Fashion	Kadın	604.0	522.0	Giyim	2871.0	Üst Giyim	604.0	T-Shirt	M	3991	train	11	28	Saturday	1	2020-11-28
5166872	2020-12-02 23:16:22+00:00	46778114.0	search	79.95	Erkek Gömlek G081GL004.000.1208586	19.0	U.S. Polo Assn.	GAS Casual	Erkek	597.0	522.0	Giyim	2871.0	Üst Giyim	597.0	Gömlek	M	3991	train	12	2	Wednesday	23	2020-12-02

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isex_perc_times_visit_Erkek_perc	search_Unisex_perc_times_visit_Kadın_perc	search_Unisex_perc_times_visit_NotDefined_perc	search_Unisex_perc_times_visit_Unisex_perc	visit_Erkek_perc_times_basket_Erkek_perc	visit_Erkek_perc_times_basket_Kadın_perc	visit_Erkek_perc_times_basket_NotDefined_perc	visit_Erkek_perc_times_basket_Unisex_perc	visit_Erkek_perc_times_favorite_Erkek_perc	visit_Erkek_perc_times_favorite_Kadın_perc	visit_Erkek_perc_times_favorite_NotDefined_perc	visit_Erkek_perc_times_favorite_Unisex_perc	visit_Erkek_perc_times_order_Erkek_perc	visit_Erkek_perc_times_order_Kadın_perc	visit_Erkek_perc_times_order_NotDefined_perc	visit_Erkek_perc_times_order_Unisex_perc	visit_Erkek_perc_times_search_Erkek_perc	visit_Erkek_perc_times_search_Kadın_perc	visit_Erkek_perc_times_search_NotDefined_perc	visit_Erkek_perc_times_search_Unisex_perc	visit_Erkek_perc_times_visit_Kadın_perc	visit_Erkek_perc_times_visit_NotDefined_perc	visit_Erkek_perc_times_visit_Unisex_perc	visit_Kadın_perc_times_basket_Erkek_perc	visit_Kadın_perc_times_basket_Kadın_perc	visit_Kadın_perc_times_basket_NotDefined_perc	visit_Kadın_perc_times_basket_Unisex_perc	visit_Kadın_perc_times_favorite_Erkek_perc	visit_Kadın_perc_times_favorite_Kadın_perc	visit_Kadın_perc_times_favorite_NotDefined_perc	visit_Kadın_perc_times_favorite_Unisex_perc	visit_Kadın_perc_times_order_Erkek_perc	visit_Kadın_perc_times_order_Kadın_perc	visit_Kadın_perc_times_order_NotDefined_perc	visit_Kadın_perc_times_order_Unisex_perc	visit_Kadın_perc_times_search_Erkek_perc	visit_Kadın_perc_times_search_Kadın_perc	visit_Kadın_perc_times_search_NotDefined_perc	visit_Kadın_perc_times_search_Unisex_perc	visit_Kadın_perc_times_visit_Erkek_perc	visit_Kadın_perc_times_visit_NotDefined_perc	visit_Kadın_perc_times_visit_Unisex_perc	visit_NotDefined_perc_times_basket_Erkek_perc	visit_NotDefined_perc_times_basket_Kadın_perc	visit_NotDefined_perc_times_basket_NotDefined_perc	visit_NotDefined_perc_times_basket_Unisex_perc	visit_NotDefined_perc_times_favorite_Erkek_perc	visit_NotDefined_perc_times_favorite_Kadın_perc	visit_NotDefined_perc_times_favorite_NotDefined_perc	visit_NotDefined_perc_times_favorite_Unisex_perc	visit_NotDefined_perc_times_order_Erkek_perc	visit_NotDefined_perc_times_order_Kadın_perc	visit_NotDefined_perc_times_order_NotDefined_perc	visit_NotDefined_perc_times_order_Unisex_perc	visit_NotDefined_perc_times_search_Erkek_perc	visit_NotDefined_perc_times_search_Kadın_perc	visit_NotDefined_perc_times_search_NotDefined_perc	visit_NotDefined_perc_times_search_Unisex_perc	visit_NotDefined_perc_times_visit_Erkek_perc	visit_NotDefined_perc_times_visit_Kadın_perc	visit_NotDefined_perc_times_visit_Unisex_perc	visit_Unisex_perc_times_basket_Erkek_perc	visit_Unisex_perc_times_basket_Kadın_perc	visit_Unisex_perc_times_basket_NotDefined_perc	visit_Unisex_perc_times_basket_Unisex_perc	visit_Unisex_perc_times_favorite_Erkek_perc	visit_Unisex_perc_times_favorite_Kadın_perc	visit_Unisex_perc_times_favorite_NotDefined_perc	visit_Unisex_perc_times_favorite_Unisex_perc	visit_Unisex_perc_times_order_Erkek_perc	visit_Unisex_perc_times_order_Kadın_perc	visit_Unisex_perc_times_order_NotDefined_perc	visit_Unisex_perc_times_order_Unisex_perc	visit_Unisex_perc_times_search_Erkek_perc	visit_Unisex_perc_times_search_Kadın_perc	visit_Unisex_perc_times_search_NotDefined_perc	visit_Unisex_perc_times_search_Unisex_perc	visit_Unisex_perc_times_visit_Erkek_perc	visit_Unisex_perc_times_visit_Kadın_perc	visit_Unisex_perc_times_visit_NotDefined_perc
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1	-1.745626	9.678430	-0.218688	2.806191	0.219172	1.363513	2.881445	1.735011	-0.613640	-0.181672	-0.591976	-0.256629	0.930715	1.002575	4.426407	14.816093	-0.183255	-0.217683	0.644712	4.839998	-0.214776	-0.070495	0.747851	7.255131	-0.207131	-0.248368	-0.335715	1.436161	-0.190194	-0.186900	-0.162721	-0.370493	2.538558	3.478547	0.024821	-0.102469	2.538558	-0.502007	-0.664187	-0.414220	3.478547	-0.502007	-0.621593	-0.393455	0.024821	-0.664187	-0.621593	-0.186904	-0.102469	-0.414220	-0.393455	-0.186904	-0.289286	-0.042021	0.261040	0.675741	-0.006875	9.653819	5.050407	9.572473	-0.294827	-0.325420	-0.304598	0.149142	-0.059347	2.129808	3.368081	3.869512	-0.194876	1.238817	0.838812	1.853536	-0.438975	0.069403	0.142581	0.916636	-0.361782	1.195656	-0.018852	0.528148	-0.361172	-0.520897	-0.448550	2.536743	-0.547547	0.622125	-0.030839	0.172512	-0.574941	0.887608	-0.354640	-0.035325	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.051153	-0.174247	0.210220	-0.138555	-0.109158	-0.182833	-0.119613	-0.242656	-0.186973	0.128329	-0.127465	-0.303237	-0.210825	-0.249916	0.411198	-0.253074	-0.164250	-0.192599	-0.233690	0.258669	-0.028934	0.743096	-0.083272	-0.214552	-0.199338	-0.182482	-0.231276	-0.315275	0.499904	-0.062188	-0.190919	-0.309397	-0.238639	1.053077	-0.163948	-0.187631	-0.203627	-0.255230	0.693073	0.837388	1.291740	-0.151391	-0.190815	-0.225495	-0.203928	-0.241291	-0.262240	1.273511	0.031644	-0.230397	-0.275348	-0.331198	1.770781	-0.227462	-0.159800	-0.236258	-0.179812	0.049300	-0.012269	-0.096383	-0.072366	-0.158005	-0.154647	-0.138663	-0.145201	-0.137481	0.098419	0.015608	-0.109182	-0.178497	-0.136194	1.317364	-0.084078	-0.139474	-0.099212	-0.144136	10.579868	4.766957	2.732960	12.530608	0.454130	0.162692	-0.090528	-0.201165	-0.157282	11.313979	1.773527	1.019782	0.420124	0.281320	15.596414	1.856668	1.412169	0.677162	-0.113592	3.317021	3.620630	1.807906	7.556452	-0.065177	0.067118	-0.121708	-0.126602	-0.153780	6.318390	0.984207	0.000818	0.459415	0.304338	9.254970	0.046962	1.742989	0.345054	-0.129301	7.103102	6.234594	3.851238	13.561978	0.035996	1.622724	-0.118220	-0.148435	-0.148026	12.881446	1.876907	0.129300	0.955887	1.128531	15.741609	0.223951	2.728140	1.237925	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	0.453078	-0.179892	-0.048382	-0.229766	0.459946	0.385883	0.724826	-0.287140	2.578283	0.056454	-0.193566	-0.334650	-0.272901	1.981734	-0.229178	1.156951	0.267756	0.286231	-0.284376	1.413253	-0.014273	0.199906	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	2.086125	1.904078	2.145108	-0.180761	4.833945	0.766702	1.981734	-0.288547	-0.430235	-0.401994	-0.097734	3.750421	1.482426	1.588713	-0.267414	4.638155	0.660904	1.245444	-0.331689	-0.227997	-0.247467	-0.282303	-0.270324	-0.059814	-0.180771	-0.229178	-0.267549	-0.263030	-0.237540	-0.097734	-0.271167	-0.381404	-0.504300	-0.396624	-0.213424	-0.312012	-0.413196	-0.307241	-0.065140	0.732134	1.874900	-0.250081	0.700332	0.304502	1.156951	-0.256082	-0.426610	-0.322679	3.750421	-0.271167	1.322029	1.727942	-0.353931	0.471453	0.183364	0.985743	-0.268618	0.382630	-0.131070	0.446549	-0.229612	1.524767	-0.118404	0.267756	-0.221375	-0.346697	-0.272488	1.482426	-0.381404	1.322029	0.049422	-0.319178	1.103162	-0.260438	-0.101201	-0.358438	0.456674	0.171898	0.390202	-0.281311	2.011044	-0.004857	0.286231	-0.292859	-0.436760	-0.359046	1.588713	-0.504300	1.727942	0.049422	-0.469588	1.545631	-0.251092	-0.132487	-0.322560	-0.288249	-0.269393	-0.354003	-0.264681	-0.238978	-0.199579	-0.284376	-0.266837	-0.256281	-0.221273	-0.267414	-0.396624	-0.353931	-0.319178	-0.469588	-0.444439	-0.413692	-0.551988	-0.290780	0.017112	1.013827	2.484809	-0.226658	0.943570	0.453078	1.413253	-0.260856	-0.436212	-0.326334	4.638155	-0.213424	0.471453	1.103162	1.545631	-0.444439	0.607587	1.957450	-0.264735	0.048376	-0.240802	0.048782	-0.217979	0.596568	-0.179892	-0.014273	-0.229338	-0.342850	-0.270205	0.660904	-0.312012	0.183364	-0.260438	-0.251092	-0.413692	0.607587	-0.339684	-0.343218	0.407548	0.095351	0.229505	-0.266993	1.778014	-0.048382	0.199906	-0.282374	-0.429890	-0.366883	1.245444	-0.413196	0.985743	-0.101201	-0.132487	-0.551988	1.957450	-0.339684
2	-1.745192	-0.311389	0.690330	0.283571	0.376474	-0.109067	0.031071	-0.443769	-0.425966	0.685534	0.188126	-0.033763	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.298781	-0.221540	-0.199843	-0.232627	0.099041	-0.109265	-0.049436	-0.210461	0.074712	-0.099587	0.024730	-0.191150	0.072989	-0.030847	0.220845	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	0.645572	0.118828	0.179414	-0.577424	0.645572	0.767209	0.598741	-0.225593	0.118828	0.767209	-0.025399	-0.190656	0.179414	0.598741	-0.025399	-0.289286	1.102423	-0.374368	-0.352752	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	1.965855	-0.304598	-0.110666	-0.292482	0.769650	-0.164639	-0.438099	-0.215228	0.089326	-0.341783	-0.335593	-0.438975	2.425847	-0.607368	-0.766499	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	2.648995	-0.448550	-0.343921	-0.616602	2.019688	-0.508710	-1.071029	-0.585105	1.836382	-0.645643	-0.908689	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	9.786395	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	0.277693	-0.186973	3.420260	-0.213060	0.155398	0.859332	8.189283	-0.189833	0.609962	5.378852	2.962389	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.283815	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.222255	-0.262240	-0.130593	-0.226850	-0.249460	-0.286382	0.139575	-0.224360	-0.244696	0.085724	-0.127049	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	3.874661	-0.293320	-0.060077	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	1.009642	-0.263030	3.900929	0.703484	-0.215933	-0.256281	3.614138	0.021156	0.401786	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	0.549256	-0.393254	-0.454399	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	1.009642	-0.401994	-0.312771	0.295319	-0.351008	-0.459726	-0.303328	0.261121	-0.373554	-0.434217	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	3.297538	-0.406844	-0.310482	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	3.900929	-0.322679	0.295319	-0.499872	0.553118	-0.606289	-0.379800	2.658314	-0.222284	-0.002494	-0.268618	0.661406	-0.340644	-0.493262	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	0.703484	-0.272488	-0.351008	-0.465634	0.553118	-0.713228	-0.326745	0.260494	-0.349103	-0.518636	-0.358438	-0.333918	-0.489091	-0.594390	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.215933	-0.359046	-0.459726	-0.611501	-0.606289	-0.713228	-0.481486	-0.546480	-0.534819	-0.682358	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	3.064528	-0.420508	-0.331886	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	3.614138	-0.326334	0.261121	-0.380893	2.658314	0.260494	-0.546480	-0.486468	-0.286588	0.035488	-0.264735	-0.048866	-0.346707	-0.498946	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	0.021156	-0.270205	-0.373554	-0.338482	-0.222284	-0.349103	-0.534819	-0.420180	-0.286588	-0.695112	-0.343218	0.290698	-0.491491	-0.588888	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	0.401786	-0.366883	-0.434217	-0.478150	-0.002494	-0.518636	-0.682358	-0.566615	0.035488	-0.695112
3	-1.744759	1.144526	5.235419	7.166721	7.402634	6.143698	6.565262	-0.292404	0.344943	-0.018778	-0.102686	-0.179184	-0.136685	-0.187824	-0.171969	-0.073129	0.090979	-0.039149	-0.041223	0.321429	0.051646	-0.242811	-0.099133	0.255966	0.053216	-0.135865	-0.113463	0.367143	-0.007447	-0.131257	-0.097364	-0.127281	-0.219865	-0.265676	-0.129907	-0.108884	-0.219865	0.854253	0.144577	-0.088782	-0.265676	0.854253	-0.023293	-0.150485	-0.129907	0.144577	-0.023293	-0.147612	-0.108884	-0.088782	-0.150485	-0.147612	2.347848	8.289531	4.835977	3.467364	0.598480	1.235410	0.796180	0.695871	3.457394	5.173639	4.844935	2.747217	0.654627	7.637885	4.746703	5.665475	0.466557	7.305205	3.220358	2.665338	-0.186912	1.333542	-0.074404	-0.317959	-0.136806	1.266571	0.138789	0.149871	0.215903	0.973480	0.562244	0.047026	-0.495731	1.196348	-0.321040	-0.341624	-0.492705	1.474690	-0.426551	-0.732821	-0.075069	-0.002052	-0.000381	0.992762	0.001111	0.423294	0.094864	0.63427	0.093268	0.295986	0.326511	0.732507	-0.111547	-0.097228	-0.105780	0.706773	-0.111739	-0.054357	-0.092039	1.219323	0.505019	0.850192	3.240923	0.320915	1.123107	1.226816	1.989949	1.072204	1.149075	1.617536	0.447117	0.085128	0.137230	0.179831	0.372326	-0.001638	0.182067	0.298234	0.724313	-0.115884	0.454568	2.000549	0.040461	1.366215	0.572296	1.622335	0.438934	0.783933	0.771196	0.097633	-0.174136	0.064757	-0.066032	0.090308	-0.157573	0.158117	0.005184	0.418929	-0.184985	0.096469	1.306079	0.001589	0.764165	0.366412	1.121578	0.179075	0.357473	0.607461	-0.002027	-0.228125	-0.119927	-0.169799	-0.013927	-0.233843	-0.111250	-0.028952	-0.023365	-0.127866	-0.104300	-0.096922	-0.044662	0.064988	-0.023147	0.170052	-0.054469	0.005624	-0.006308	-0.011810	-0.108979	-0.154890	-0.122975	-0.001285	-0.084683	-0.134497	-0.087569	0.094365	-0.121743	-0.076891	-0.068565	0.390305	0.299351	0.387092	0.304091	0.039376	0.089988	0.114947	-0.063774	-0.127111	-0.124794	-0.112519	-0.063735	-0.166770	-0.112329	-0.095781	0.012628	-0.110948	-0.102151	-0.093702	0.268320	-0.029538	0.497924	0.283680	-0.036132	0.007627	0.037404	-0.092951	-0.138653	-0.139965	-0.156709	-0.080798	-0.141938	-0.122608	-0.085619	0.001896	-0.124313	-0.112717	-0.106234	0.212590	-0.051320	0.583374	0.229137	-0.051113	-0.009629	0.019947	-0.095894	-0.150844	-0.193432	-0.201942	-0.083427	-0.158499	-0.171668	-0.118397	0.798910	-0.081884	0.075799	0.045959	1.613839	0.058355	...	1.137813	-0.172487	-0.198678	-0.055500	1.195451	0.002390	-0.182916	-0.018325	1.641428	0.020457	0.180770	0.789296	0.384099	-0.018501	-0.215514	1.043306	-0.143298	-0.159218	-0.226015	1.209698	-0.191508	-0.292368	-0.202471	1.404680	0.195481	0.074671	-0.123111	1.215453	0.223577	0.180770	1.275850	1.091158	0.391438	-0.238186	1.242371	0.084921	0.111393	-0.245668	1.570119	0.024864	-0.057889	0.376013	0.899225	0.733932	0.518508	0.265761	1.120990	0.687660	0.789296	1.275850	2.580513	1.169791	-0.019618	1.034578	0.563738	0.769008	-0.043292	1.222235	0.369528	0.260807	0.142337	2.359649	-0.073871	0.125568	0.110118	1.774445	0.136710	0.384099	1.091158	2.580513	0.430768	-0.101577	1.744018	-0.067109	0.166425	-0.112051	2.051168	-0.134837	-0.060084	-0.041917	1.047012	-0.043403	-0.295842	-0.032194	0.811889	0.023756	-0.018501	0.391438	1.169791	0.430768	-0.232116	0.801114	-0.168497	-0.201272	-0.233367	0.999650	-0.211391	-0.331612	-0.320115	0.048035	-0.234168	-0.330887	-0.258703	0.124678	-0.137798	-0.215514	-0.238186	-0.019618	-0.101577	-0.232116	0.053301	-0.379186	-0.497366	-0.392831	0.007700	-0.301391	-0.436269	0.510031	1.238619	0.711668	0.467147	0.522243	1.232919	0.915939	1.043306	1.242371	1.034578	1.744018	0.801114	0.053301	0.942889	1.313736	-0.054831	1.406872	0.251981	0.172420	-0.136156	0.870380	-0.253314	-0.338066	-0.110697	0.772778	-0.145456	-0.143298	0.084921	0.563738	-0.067109	-0.168497	-0.379186	0.942889	-0.449578	-0.286408	0.737600	-0.310566	-0.451852	-0.177170	1.117700	-0.203362	-0.413281	-0.126749	1.081909	-0.065637	-0.159218	0.111393	0.769008	0.166425	-0.201272	-0.497366	1.313736	-0.449578	-0.415691	1.105211	-0.398150	-0.563889	-0.314030	0.028378	-0.223702	-0.324247	-0.255720	0.068462	-0.143033	-0.226015	-0.245668	-0.043292	-0.112051	-0.233367	-0.392831	-0.054831	-0.286408	-0.415691	0.020041	-0.372964	-0.517634	0.567323	1.482361	0.930789	0.660743	0.515405	1.497484	1.137813	1.209698	1.570119	1.222235	2.051168	0.999650	0.007700	1.406872	0.737600	1.105211	0.020041	0.685195	0.513048	-0.165970	0.647681	-0.286456	-0.369039	-0.124402	0.437058	-0.172487	-0.191508	0.024864	0.369528	-0.134837	-0.211391	-0.301391	0.251981	-0.310566	-0.398150	-0.372964	0.685195	-0.600830	-0.243852	0.407848	-0.337865	-0.504890	-0.189086	0.381678	-0.198678	-0.292368	-0.057889	0.260807	-0.060084	-0.331612	-0.436269	0.172420	-0.451852	-0.563889	-0.517634	0.513048	-0.600830
4	-1.744326	-0.300190	0.589328	1.660201	4.912017	7.098823	4.797081	-0.442214	1.524188	-1.076669	-0.154981	-0.242937	-0.180343	-0.265400	-0.230131	-0.147677	42.327820	1.442881	0.176503	4.029844	10.162181	-0.464019	-0.224615	0.619821	23.212860	-0.202562	0.284209	2.485198	4.073385	-0.185345	-0.123386	-0.432862	-0.597250	-0.576221	-0.224696	-0.190456	-0.597250	-0.334059	0.501896	-0.304700	-0.576221	-0.334059	-0.493830	-0.387759	-0.224696	0.501896	-0.493830	-0.178778	-0.190456	-0.304700	-0.387759	-0.178778	-0.289286	7.236642	0.006877	2.291944	-0.249017	-0.248010	-0.267377	-0.271230	-0.294827	1.278472	-0.304598	0.408949	-0.146772	1.730753	0.007689	2.195080	-0.143997	7.660683	1.408755	6.870290	-0.438975	1.902588	-0.550298	-0.306173	-0.371557	2.413708	-0.442268	-0.632624	-0.361172	1.919920	-0.448550	0.318631	-0.550491	1.053459	-0.603126	0.098651	-0.576678	1.268200	-0.589910	-0.300222	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	8.787491	1.147153	-0.170886	32.681712	-0.117074	-0.187267	-0.119613	3.713914	-0.186973	6.241916	3.553107	1.102130	3.385943	0.634858	4.532555	-0.049721	0.474646	-0.024340	-0.233690	-0.237582	-0.305967	-0.193304	0.661539	-0.254071	-0.208695	-0.182482	-0.063139	-0.315275	0.055971	-0.068465	-0.194000	-0.012518	-0.239577	-0.003196	-0.172241	-0.302431	-0.237305	-0.255230	-0.207815	4.887749	-0.213823	8.879607	-0.248114	-0.242424	-0.203928	0.764867	-0.262240	2.317588	0.793338	-0.187148	1.623621	-0.008600	1.181739	-0.239397	0.007603	-0.266170	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.172973	-0.152635	-0.191765	-0.158600	-0.182845	-0.252054	-0.090528	-0.190984	-0.157282	-0.160644	-0.129190	-0.181257	-0.183479	-0.158491	-0.126431	-0.230724	-0.192755	-0.192308	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	2.332442	-0.113658	1.137723	-0.176098	3.526284	-0.253803	-0.334650	-0.262986	-0.319909	3.228492	-0.045577	1.795890	0.120637	2.618788	-0.224554	2.038532	0.162121	1.898008	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	2.201300	-0.340807	-0.222083	-0.216594	2.248013	-0.285727	-0.356228	-0.288547	3.228492	-0.401994	-0.251011	1.177957	-0.301358	0.113178	-0.294395	1.402494	-0.282269	-0.076322	-0.331689	-0.055967	-0.298018	-0.355650	-0.270608	0.074594	-0.211421	-0.305114	-0.267549	-0.045577	-0.237540	-0.251011	-0.218118	-0.450774	-0.514647	-0.396677	-0.186716	-0.329678	-0.430882	-0.307241	1.587109	-0.295311	0.416939	-0.274077	2.101125	-0.377665	-0.502668	-0.256082	1.795890	-0.322679	1.177957	-0.218118	-0.202259	2.284851	-0.352201	1.099483	-0.173819	0.949526	-0.268618	0.088393	-0.337705	-0.454647	-0.238703	0.191962	-0.256889	-0.411976	-0.221375	0.120637	-0.272488	-0.301358	-0.450774	-0.202259	-0.591330	-0.325589	-0.172038	-0.348864	-0.485809	-0.358438	2.780692	-0.441622	-0.305679	-0.292669	3.458532	-0.372620	-0.468823	-0.292859	2.618788	-0.359046	0.113178	-0.514647	2.284851	-0.591330	-0.472177	1.870793	-0.443743	-0.289640	-0.322560	-0.262962	-0.277233	-0.366358	-0.264723	-0.225058	-0.204506	-0.295756	-0.266837	-0.224554	-0.221273	-0.294395	-0.396677	-0.352201	-0.325589	-0.472177	-0.444265	-0.418311	-0.557397	-0.290780	1.834166	-0.288315	0.577672	-0.250723	2.440514	-0.382756	-0.500808	-0.260856	2.038532	-0.326334	1.402494	-0.186716	1.099483	-0.172038	1.870793	-0.444265	-0.126366	1.733290	-0.264735	0.156483	-0.343907	-0.444567	-0.223114	0.197947	-0.255007	-0.399279	-0.229338	0.162121	-0.270205	-0.282269	-0.329678	-0.173819	-0.348864	-0.443743	-0.418311	-0.126366	-0.612239	-0.343218	1.986356	-0.457547	-0.401421	-0.276922	2.382697	-0.352323	-0.454993	-0.282374	1.898008	-0.366883	-0.076322	-0.430882	0.949526	-0.485809	-0.289640	-0.557397	1.733290	-0.612239
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5613	1.714648	-0.311389	-0.319690	-0.494838	-0.462470	-0.416804	-0.472841	-0.443769	-1.880614	-1.713117	-0.700702	7.944392	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.521460	-0.363079	-0.363376	-0.232627	-0.536008	-0.312403	-0.376642	-0.210461	-0.301157	-0.391677	-0.382169	-0.191150	-0.195333	-0.168628	-0.540969	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	-1.939581	-0.757560	-0.428719	-0.577424	-1.939581	-0.739247	-0.411104	-0.225593	-0.757560	-0.739247	-0.187446	-0.190656	-0.428719	-0.411104	-0.187446	-0.289286	-0.316688	-0.374368	-0.426215	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	-0.325420	-0.304598	-0.110666	-0.292482	-0.365689	-0.451852	-0.492113	-0.215228	-0.322630	-0.382493	-0.417685	-0.438975	-0.667466	-0.607368	-0.862890	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	-0.520897	-0.448550	2.536743	-0.616602	-0.881689	-0.732407	-1.139955	-0.585105	-0.915103	-0.715166	-1.139595	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	-0.304451	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	-0.242656	-0.186973	-0.224378	-0.213060	-0.347522	-0.226628	-0.270192	-0.189833	-0.299516	-0.200244	-0.214826	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.296585	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.241291	-0.262240	-0.346609	-0.226850	-0.252734	-0.372710	-0.438599	-0.224360	-0.250523	-0.405325	-0.365585	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	-0.399226	-0.393254	-0.467263	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	-0.430235	-0.401994	-0.312771	-0.439588	-0.405740	-0.469553	-0.303328	-0.444062	-0.392013	-0.466765	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	-0.538808	-0.406844	-0.565521	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	-0.426610	-0.322679	-0.439588	-0.499872	-0.676062	-0.869467	-0.379800	-0.653031	-0.462628	-0.670794	-0.268618	-0.440892	-0.340644	-0.509779	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	-0.346697	-0.272488	-0.405740	-0.465634	-0.676062	-0.726216	-0.326745	-0.538398	-0.354912	-0.548456	-0.358438	-0.557506	-0.489091	-0.597199	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.436760	-0.359046	-0.469553	-0.611501	-0.869467	-0.726216	-0.481486	-0.728790	-0.537722	-0.688463	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	-0.546463	-0.420508	-0.585743	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	-0.436212	-0.326334	-0.444062	-0.380893	-0.653031	-0.538398	-0.728790	-0.486468	-0.659546	-0.865457	-0.264735	-0.462146	-0.346707	-0.504728	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	-0.342850	-0.270205	-0.392013	-0.338482	-0.462628	-0.354912	-0.537722	-0.420180	-0.659546	-0.710046	-0.343218	-0.548649	-0.491491	-0.598264	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
5614	1.715081	-0.311389	-0.420692	-0.494838	-0.462470	-0.414525	-0.472841	-0.443769	2.364803	-1.713117	-0.700702	-0.262901	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.521460	-0.363079	-0.363376	-0.232627	-0.536008	-0.312403	-0.376642	-0.210461	-0.301157	-0.391677	-0.382169	-0.191150	-0.195333	-0.168628	-0.540969	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	-1.939581	-0.757560	-0.428719	-0.577424	-1.939581	-0.739247	-0.411104	-0.225593	-0.757560	-0.739247	-0.187446	-0.190656	-0.428719	-0.411104	-0.187446	-0.289286	-0.316688	-0.374368	-0.426215	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	-0.325420	-0.304598	-0.370473	-0.292482	-0.365689	-0.451852	-0.492113	-0.215228	-0.322630	-0.382493	-0.408564	-0.438975	-0.667466	-0.607368	-0.862890	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	-0.520897	-0.448550	-0.631988	-0.616602	-0.881689	-0.732407	-1.139955	-0.585105	-0.915103	-0.715166	2.323999	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	-0.304451	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	-0.242656	-0.186973	-0.224378	-0.213060	-0.347522	-0.226628	-0.270192	-0.189833	-0.299516	-0.200244	-0.214826	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.296585	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.241291	-0.262240	-0.346609	-0.226850	-0.252734	-0.372710	-0.438599	-0.224360	-0.250523	-0.405325	-0.365585	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	-0.399226	-0.393254	-0.467263	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	-0.430235	-0.401994	-0.312771	-0.439588	-0.405740	-0.469553	-0.303328	-0.444062	-0.392013	-0.466765	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	-0.538808	-0.406844	-0.565521	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	-0.426610	-0.322679	-0.439588	-0.499872	-0.676062	-0.869467	-0.379800	-0.653031	-0.462628	-0.670794	-0.268618	-0.440892	-0.340644	-0.509779	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	-0.346697	-0.272488	-0.405740	-0.465634	-0.676062	-0.726216	-0.326745	-0.538398	-0.354912	-0.548456	-0.358438	-0.557506	-0.489091	-0.597199	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.436760	-0.359046	-0.469553	-0.611501	-0.869467	-0.726216	-0.481486	-0.728790	-0.537722	-0.688463	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	-0.546463	-0.420508	-0.585743	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	-0.436212	-0.326334	-0.444062	-0.380893	-0.653031	-0.538398	-0.728790	-0.486468	-0.659546	-0.865457	-0.264735	-0.462146	-0.346707	-0.504728	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	-0.342850	-0.270205	-0.392013	-0.338482	-0.462628	-0.354912	-0.537722	-0.420180	-0.659546	-0.710046	-0.343218	-0.548649	-0.491491	-0.598264	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
5615	1.716380	-0.311389	-0.420692	-0.494838	-0.462470	-0.414525	-0.472841	-0.443769	2.364803	-1.713117	-0.700702	-0.262901	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.521460	-0.363079	-0.363376	-0.232627	-0.536008	-0.312403	-0.376642	-0.210461	-0.301157	-0.391677	-0.382169	-0.191150	-0.195333	-0.168628	-0.540969	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	-1.939581	-0.757560	-0.428719	-0.577424	-1.939581	-0.739247	-0.411104	-0.225593	-0.757560	-0.739247	-0.187446	-0.190656	-0.428719	-0.411104	-0.187446	-0.289286	-0.316688	-0.374368	-0.426215	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	-0.325420	-0.304598	-0.370473	-0.292482	-0.365689	-0.451852	-0.492113	-0.215228	-0.322630	-0.362138	-0.417685	-0.438975	-0.667466	-0.607368	-0.862890	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	-0.520897	-0.448550	-0.631988	-0.616602	-0.881689	-0.732407	-1.139955	-0.585105	-0.915103	3.977684	-1.139595	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	-0.304451	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	-0.242656	-0.186973	-0.224378	-0.213060	-0.347522	-0.226628	-0.270192	-0.189833	-0.299516	-0.200244	-0.214826	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.296585	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.241291	-0.262240	-0.346609	-0.226850	-0.252734	-0.372710	-0.438599	-0.224360	-0.250523	-0.405325	-0.365585	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	-0.399226	-0.393254	-0.467263	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	-0.430235	-0.401994	-0.312771	-0.439588	-0.405740	-0.469553	-0.303328	-0.444062	-0.392013	-0.466765	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	-0.538808	-0.406844	-0.565521	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	-0.426610	-0.322679	-0.439588	-0.499872	-0.676062	-0.869467	-0.379800	-0.653031	-0.462628	-0.670794	-0.268618	-0.440892	-0.340644	-0.509779	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	-0.346697	-0.272488	-0.405740	-0.465634	-0.676062	-0.726216	-0.326745	-0.538398	-0.354912	-0.548456	-0.358438	-0.557506	-0.489091	-0.597199	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.436760	-0.359046	-0.469553	-0.611501	-0.869467	-0.726216	-0.481486	-0.728790	-0.537722	-0.688463	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	-0.546463	-0.420508	-0.585743	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	-0.436212	-0.326334	-0.444062	-0.380893	-0.653031	-0.538398	-0.728790	-0.486468	-0.659546	-0.865457	-0.264735	-0.462146	-0.346707	-0.504728	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	-0.342850	-0.270205	-0.392013	-0.338482	-0.462628	-0.354912	-0.537722	-0.420180	-0.659546	-0.710046	-0.343218	-0.548649	-0.491491	-0.598264	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
5616	1.716813	-0.311389	-0.420692	-0.494838	-0.436253	-0.414525	-0.471559	-0.443769	0.242095	-1.713117	4.771148	-0.262901	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.521460	-0.329529	-0.363376	-0.232627	-0.536008	-0.312403	-0.376642	-0.210461	1.284538	-0.391677	-0.382169	-0.191150	-0.195333	-0.168628	-0.540969	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	-1.939581	7.115531	-0.428719	-0.577424	-1.939581	-0.739247	-0.411104	-0.225593	7.115531	-0.739247	-0.187446	-0.190656	-0.428719	-0.411104	-0.187446	-0.289286	-0.316688	-0.374368	-0.352752	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	-0.325420	-0.304598	-0.370473	-0.292482	-0.365689	-0.451852	-0.492113	-0.215228	-0.322630	-0.382493	-0.408564	-0.438975	-0.667466	-0.607368	2.221621	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	-0.520897	-0.448550	-0.631988	-0.616602	-0.881689	-0.732407	-1.139955	-0.585105	-0.915103	-0.715166	2.323999	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	-0.304451	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	-0.242656	-0.186973	-0.224378	-0.213060	-0.347522	-0.226628	-0.270192	-0.189833	-0.299516	-0.200244	-0.214826	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.296585	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.241291	-0.262240	-0.346609	-0.226850	-0.252734	-0.372710	-0.438599	-0.224360	-0.250523	-0.405325	1.781243	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	-0.399226	-0.393254	-0.467263	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	-0.430235	-0.401994	-0.312771	-0.439588	-0.405740	-0.469553	-0.303328	-0.444062	-0.392013	-0.466765	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	-0.538808	-0.406844	-0.565521	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	-0.426610	-0.322679	-0.439588	-0.499872	-0.676062	-0.869467	-0.379800	-0.653031	-0.462628	-0.670794	-0.268618	-0.440892	-0.340644	-0.509779	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	-0.346697	-0.272488	-0.405740	-0.465634	-0.676062	-0.726216	-0.326745	-0.538398	-0.354912	-0.548456	-0.358438	-0.557506	-0.489091	-0.597199	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.436760	-0.359046	-0.469553	-0.611501	-0.869467	-0.726216	-0.481486	-0.728790	-0.537722	-0.688463	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	-0.546463	-0.420508	-0.585743	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	-0.436212	-0.326334	-0.444062	-0.380893	-0.653031	-0.538398	-0.728790	-0.486468	-0.659546	-0.865457	-0.264735	-0.462146	-0.346707	-0.504728	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	-0.342850	-0.270205	-0.392013	-0.338482	-0.462628	-0.354912	-0.537722	-0.420180	-0.659546	-0.710046	-0.343218	-0.548649	-0.491491	3.902049	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
5617	1.717246	-0.311389	-0.420692	-0.494838	-0.462470	-0.414525	-0.472841	-0.443769	2.364803	-1.713117	-0.700702	-0.262901	-0.180638	-0.267581	-0.230793	-0.151032	-0.194547	-0.521460	-0.363079	-0.363376	-0.232627	-0.536008	-0.312403	-0.376642	-0.210461	-0.301157	-0.391677	-0.382169	-0.191150	-0.195333	-0.168628	-0.540969	-0.603276	-0.577424	-0.225593	-0.190656	-0.603276	-1.939581	-0.757560	-0.428719	-0.577424	-1.939581	-0.739247	-0.411104	-0.225593	-0.757560	-0.739247	-0.187446	-0.190656	-0.428719	-0.411104	-0.187446	-0.289286	-0.316688	-0.374368	-0.426215	-0.249017	-0.266553	-0.267377	-0.271230	-0.294827	-0.325420	-0.304598	-0.370473	-0.292482	-0.365689	-0.451852	-0.492113	-0.215228	-0.322630	-0.362138	-0.417685	-0.438975	-0.667466	-0.607368	-0.862890	-0.371557	-0.629716	-0.442268	-0.632624	-0.361172	-0.520897	-0.448550	-0.631988	-0.616602	-0.881689	-0.732407	-1.139955	-0.585105	-0.915103	3.977684	-1.139595	-0.107670	-0.185137	-0.214146	-0.164489	-0.094570	-0.152272	-0.113455	-0.17798	-0.143848	-0.174327	-0.201379	-0.305050	-0.118346	-0.164923	-0.160321	-0.279759	-0.115667	-0.095353	-0.182028	-0.189275	-0.248297	-0.304451	-0.170886	-0.140786	-0.117074	-0.187267	-0.119613	-0.242656	-0.186973	-0.224378	-0.213060	-0.347522	-0.226628	-0.270192	-0.189833	-0.299516	-0.200244	-0.214826	-0.233690	-0.248651	-0.395014	-0.193304	-0.085599	-0.254071	-0.208695	-0.182482	-0.231276	-0.315275	-0.239984	-0.219112	-0.201700	-0.383849	-0.274536	-0.221735	-0.173344	-0.381908	-0.245050	-0.255230	-0.296585	-0.464513	-0.213823	-0.158239	-0.248114	-0.242424	-0.203928	-0.241291	-0.262240	-0.346609	-0.226850	-0.252734	-0.372710	-0.438599	-0.224360	-0.250523	-0.405325	-0.365585	-0.179812	-0.150144	-0.172834	-0.153534	-0.073618	-0.171520	-0.158445	-0.138663	-0.145201	-0.137481	-0.152926	-0.182413	-0.111404	-0.193516	-0.145132	-0.155128	-0.086211	-0.166168	-0.106588	-0.144136	-0.173704	-0.168009	-0.193983	-0.158600	-0.182845	-0.252054	-0.090528	-0.201165	-0.157282	-0.174960	-0.134897	-0.183935	-0.193006	-0.159829	-0.134832	-0.231488	-0.194803	-0.192815	-0.113592	-0.132969	-0.195220	-0.207799	-0.173385	-0.176207	-0.348832	-0.121708	-0.126602	-0.153780	-0.179181	-0.148726	-0.147929	-0.243226	-0.232804	-0.136869	-0.150002	-0.258287	-0.160038	-0.129301	-0.147046	-0.189687	-0.219420	-0.172952	-0.212702	-0.420136	-0.118220	-0.148435	-0.148026	-0.194156	-0.144785	-0.159697	-0.282859	-0.301456	-0.129974	-0.166348	-0.271569	-0.224221	-0.285469	-0.145927	-0.186717	-0.189680	-0.174104	-0.097035	...	-0.382756	-0.255007	-0.352323	-0.229766	-0.425566	-0.350290	-0.419045	-0.304838	-0.547748	-0.424114	-0.193566	-0.334650	-0.272901	-0.356228	-0.305114	-0.502668	-0.411976	-0.468823	-0.295756	-0.500808	-0.399279	-0.454993	-0.253731	-0.231683	-0.188741	-0.261407	-0.187412	-0.195287	-0.150232	-0.193566	-0.262986	-0.209364	-0.288547	-0.267549	-0.256082	-0.221375	-0.292859	-0.266837	-0.260856	-0.229338	-0.282374	-0.229067	-0.406287	-0.293320	-0.407545	-0.176098	-0.362478	-0.253803	-0.334650	-0.262986	-0.319909	-0.430235	-0.263030	-0.426610	-0.346697	-0.436760	-0.256281	-0.436212	-0.342850	-0.429890	-0.185996	-0.298522	-0.290598	-0.362760	-0.158025	-0.250860	-0.207626	-0.272901	-0.209364	-0.319909	-0.401994	-0.237540	-0.322679	-0.272488	-0.359046	-0.221273	-0.326334	-0.270205	-0.366883	-0.263504	-0.399226	-0.393254	-0.467263	-0.216594	-0.339853	-0.285727	-0.356228	-0.288547	-0.430235	-0.401994	-0.312771	-0.439588	-0.405740	-0.469553	-0.303328	-0.444062	-0.392013	-0.466765	-0.331689	-0.301544	-0.301991	-0.387011	-0.270608	-0.285257	-0.211421	-0.305114	-0.267549	-0.263030	-0.237540	-0.312771	-0.499872	-0.465634	-0.611501	-0.396879	-0.380893	-0.338482	-0.478150	-0.307241	-0.538808	-0.406844	-0.565521	-0.274077	-0.522520	-0.377665	-0.502668	-0.256082	-0.426610	-0.322679	-0.439588	-0.499872	-0.676062	-0.869467	-0.379800	-0.653031	-0.462628	-0.670794	-0.268618	-0.440892	-0.340644	-0.509779	-0.238703	-0.399131	-0.256889	-0.411976	-0.221375	-0.346697	-0.272488	-0.405740	-0.465634	-0.676062	-0.726216	-0.326745	-0.538398	-0.354912	-0.548456	-0.358438	-0.557506	-0.489091	-0.597199	-0.292669	-0.513084	-0.372620	-0.468823	-0.292859	-0.436760	-0.359046	-0.469553	-0.611501	-0.869467	-0.726216	-0.481486	-0.728790	-0.537722	-0.688463	-0.322560	-0.301616	-0.277761	-0.370684	-0.264723	-0.275387	-0.204506	-0.295756	-0.266837	-0.256281	-0.221273	-0.303328	-0.396879	-0.379800	-0.326745	-0.481486	-0.486468	-0.420180	-0.566615	-0.290780	-0.546463	-0.420508	-0.585743	-0.250723	-0.525042	-0.382756	-0.500808	-0.260856	-0.436212	-0.326334	-0.444062	-0.380893	-0.653031	-0.538398	-0.728790	-0.486468	-0.659546	-0.865457	-0.264735	-0.462146	-0.346707	-0.504728	-0.223114	-0.350864	-0.255007	-0.399279	-0.229338	-0.342850	-0.270205	-0.392013	-0.338482	-0.462628	-0.354912	-0.537722	-0.420180	-0.659546	-0.710046	-0.343218	-0.548649	-0.491491	-0.598264	-0.276922	-0.483901	-0.352323	-0.454993	-0.282374	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046

	Model	MAE	MSE	RMSE	R2	RMSLE	MAPE	TT (Sec)
gbr	Gradient Boosting Regressor	0.2856	0.1418	0.3763	0.3753	0.2643	0.2215	2.1650
catboost	CatBoost Regressor	0.2825	0.1450	0.3807	0.3610	0.2665	0.2229	29.4790
br	Bayesian Ridge	0.3086	0.1465	0.3825	0.3547	0.2687	0.2465	0.2540
lightgbm	Light Gradient Boosting Machine	0.2786	0.1488	0.3856	0.3444	0.2697	0.2184	1.5400
rf	Random Forest Regressor	0.2854	0.1524	0.3903	0.3280	0.2730	0.2211	6.1250
omp	Orthogonal Matching Pursuit	0.3052	0.1557	0.3940	0.3148	0.2727	0.2455	0.0300
ada	AdaBoost Regressor	0.3502	0.1613	0.4015	0.2887	0.2820	0.2836	0.9440
et	Extra Trees Regressor	0.2878	0.1644	0.4053	0.2752	0.2834	0.2232	4.0490
knn	K Neighbors Regressor	0.2924	0.1750	0.4182	0.2278	0.2934	0.2073	0.0430
huber	Huber Regressor	0.3073	0.1994	0.4449	0.1234	0.2929	0.2467	0.9040
ridge	Ridge Regression	0.3364	0.2160	0.4599	0.0513	0.2925	0.2775	0.2040
lr	Linear Regression	0.3390	0.2203	0.4646	0.0327	0.2944	0.2797	0.3500
lasso	Lasso Regression	0.4561	0.2282	0.4776	-0.0052	0.3347	0.3515	0.2140
en	Elastic Net	0.4561	0.2282	0.4776	-0.0052	0.3347	0.3515	0.1910
llar	Lasso Least Angle Regression	0.4561	0.2282	0.4776	-0.0052	0.3347	0.3515	0.0290
dt	Decision Tree Regressor	0.2843	0.2843	0.5331	-0.2552	0.3695	0.2190	0.1370
par	Passive Aggressive Regressor	0.4653	0.3897	0.6232	-0.7207	0.3575	0.4183	0.0510
lar	Least Angle Regression	45254983.9215	67592779986475144.0000	82343722.4389	-288690854747003968.0000	6.0463	43373165.2788	0.0900

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	0.3089	0.1528	0.3909	0.3484	0.2767	0.2358
1	0.2930	0.1401	0.3744	0.3686	0.2610	0.2368
2	0.2955	0.1535	0.3918	0.3444	0.2785	0.2107
3	0.3012	0.1521	0.3900	0.3301	0.2741	0.2269
4	0.2893	0.1389	0.3727	0.3944	0.2615	0.2290
5	0.3129	0.1617	0.4021	0.2777	0.2798	0.2585
6	0.2839	0.1367	0.3697	0.3848	0.2573	0.2283
7	0.2679	0.1280	0.3578	0.4172	0.2527	0.1942
8	0.2899	0.1497	0.3869	0.3430	0.2745	0.2019
9	0.2873	0.1399	0.3741	0.3817	0.2609	0.2328
Mean	0.2930	0.1454	0.3810	0.3590	0.2677	0.2255
SD	0.0122	0.0096	0.0127	0.0373	0.0095	0.0178

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	0.3206	0.1582	0.3977	0.3255	0.2828	0.2415
1	0.3093	0.1513	0.3890	0.3183	0.2702	0.2535
2	0.3117	0.1606	0.4007	0.3141	0.2857	0.2248
3	0.3036	0.1504	0.3878	0.3379	0.2715	0.2413
4	0.3016	0.1455	0.3814	0.3657	0.2677	0.2429
5	0.3216	0.1635	0.4043	0.2698	0.2787	0.2737
6	0.2981	0.1457	0.3817	0.3444	0.2647	0.2495
7	0.2810	0.1331	0.3648	0.3940	0.2581	0.2043
8	0.3030	0.1566	0.3958	0.3126	0.2794	0.2197
9	0.2966	0.1423	0.3772	0.3713	0.2639	0.2388
Mean	0.3047	0.1507	0.3880	0.3354	0.2723	0.2390
SD	0.0114	0.0089	0.0115	0.0337	0.0086	0.0182

PCA Denemeleri¶

input data has 984 columns. Model Accuracy is %80,31¶

I want to know the model accuracy with smaller complexity input data¶

when we have %90 variance explained by PCA. It is smaller, checked.¶

CATEGORY NAMES IN DATA¶

Category visualization analysis¶

	time_stamp	contentid	user_action	sellingprice	product_name	brand_id	brand_name	businessunit	product_gender	category_id	...	Level3_Category_Id	Level3_Category_Name	gender	unique_id	type	month	dayOfMonth	dayofWeek	hour	date
0	2020-12-01 00:46:25+00:00	55075555.0	visit	89.99	Kadın Pembe Kareli Uçları Saçaklı Uzun Atkı 17...	1867.0	PIECES	Kadın A	Kadın	382.0	...	382.0	Atkı	NaN	108	test	12	1	Tuesday	0	2020-12-01
1	2020-12-03 19:26:19+00:00	32026013.0	visit	27.11	12835 Siyah Bere	33496.0	Axesoire	Diğer Aksesuarlar	Kadın	384.0	...	384.0	Bere	NaN	108	test	12	3	Thursday	19	2020-12-03
2	2020-11-12 21:55:33+00:00	4237521.0	visit	169.90	K 909 Perfection Kırmızı Saç Düzleştirici Saç ...	11022.0	King	Kişisel Bakım Aletleri	Kadın	865.0	...	865.0	Saç Düzleştirici	NaN	108	test	11	12	Thursday	21	2020-11-12
3	2020-11-02 02:07:38+00:00	33388105.0	visit	59.90	Pratik Isıtıcı + 1 Adet 200g Pudralı	25522.0	İnci Ağda	Kişisel Bakım	NaN	2431.0	...	1542.0	Ağda & Tüy Dökücü	NaN	108	test	11	2	Monday	2	2020-11-02
4	2020-11-19 18:07:42+00:00	32433838.0	visit	38.99	Atkı	842.0	Koton	Kadın A	Kadın	382.0	...	382.0	Atkı	NaN	108	test	11	19	Thursday	18	2020-11-19
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2324809	2020-12-08 22:42:09+00:00	39832331.0	visit	0.00	3 Katlı Telli Cerrahi Maske 1 Kutu 50 Adet Kut...	972528.0	masktak	Sağlık	NaN	4025.0	...	4025.0	Medikal Maske	NaN	7509	test	12	8	Tuesday	22	2020-12-08
2324810	2020-12-08 22:42:14+00:00	46129448.0	basket	71.10	Yumuşak Elastik Kulaklı Maske 100 Adet	33938.0	Evony	Sağlık	NaN	4025.0	...	4025.0	Medikal Maske	NaN	7509	test	12	8	Tuesday	22	2020-12-08
2324811	2020-12-08 22:42:16+00:00	46881661.0	visit	39.29	Yumuşak Elastik Kulaklı 3 Katlı Cerrahi Maske ...	33938.0	Evony	Sağlık	NaN	4025.0	...	4025.0	Medikal Maske	NaN	7509	test	12	8	Tuesday	22	2020-12-08
2324812	2020-12-08 22:42:07+00:00	43524099.0	basket	0.00	50 Adet Siyah Tek Kullanımlık Lastikli Telli 3...	961001.0	Commando	Sağlık	NaN	4025.0	...	4025.0	Medikal Maske	NaN	7509	test	12	8	Tuesday	22	2020-12-08
2324813	2020-12-08 22:42:05+00:00	43524099.0	visit	0.00	50 Adet Siyah Tek Kullanımlık Lastikli Telli 3...	961001.0	Commando	Sağlık	NaN	4025.0	...	4025.0	Medikal Maske	NaN	7509	test	12	8	Tuesday	22	2020-12-08

	0	1	2
time_stamp	2020-12-02 22:26:14.023000+00:00	2020-12-08 23:15:04.603000+00:00	2020-12-05 16:19:01.157000+00:00
contentid	39918893.0	3558544.0	31292729.0
user_action	favorite	favorite	favorite
sellingprice	3099.0	3079.0	3999.0
product_name	PerfectCare 600 EW6F449ST A+++ 9 KG 1400 Devir...	WW90J5475FW A+++ 1400 Devir 9 kg Çamaşır Makinesi	KM 9711 A++ 9 kg Çamaşır Kurutma Makinesi
brand_id	8511.0	3228.0	10989.0
brand_name	Electrolux	Samsung	Vestel
businessunit	Beyaz Eşya	Beyaz Eşya	Beyaz Eşya
product_gender	Unisex	NaN	Unisex
category_id	1272.0	1272.0	1276.0
Level1_Category_Id	1071.0	1071.0	1071.0
Level1_Category_Name	Elektronik	Elektronik	Elektronik
Level2_Category_Id	1212.0	1212.0	1212.0
Level2_Category_Name	Beyaz Eşya	Beyaz Eşya	Beyaz Eşya
Level3_Category_Id	1272.0	1272.0	1276.0
Level3_Category_Name	Çamaşır Makinesi	Çamaşır Makinesi	Kurutma Makinesi
gender	F	F	F
unique_id	425	425	425
type	train	train	train
month	12	12	12
dayOfMonth	2	8	5
dayofWeek	Wednesday	Tuesday	Saturday
hour	22	23	16
date	2020-12-02 00:00:00	2020-12-08 00:00:00	2020-12-05 00:00:00

	0	1	2
time_stamp	2020-12-01T00:46:25Z	2020-12-03T19:26:19Z	2020-11-12T21:55:33Z
contentid	55075555.0	32026013.0	4237521.0
user_action	visit	visit	visit
sellingprice	89.99	27.11	169.9
product_name	Kadın Pembe Kareli Uçları Saçaklı Uzun Atkı 17...	12835 Siyah Bere	K 909 Perfection Kırmızı Saç Düzleştirici Saç ...
brand_id	1867.0	33496.0	11022.0
brand_name	PIECES	Axesoire	King
businessunit	Kadın A	Diğer Aksesuarlar	Kişisel Bakım Aletleri
product_gender	Kadın	Kadın	Kadın
category_id	382.0	384.0	865.0
Level1_Category_Id	368.0	368.0	1071.0
Level1_Category_Name	Aksesuar	Aksesuar	Elektronik
Level2_Category_Id	1162.0	1162.0	2373.0
Level2_Category_Name	Atkı & Bere & Eldiven	Atkı & Bere & Eldiven	Kişisel Bakım Aletleri
Level3_Category_Id	382.0	384.0	865.0
Level3_Category_Name	Atkı	Bere	Saç Düzleştirici
gender	NaN	NaN	NaN
unique_id	108	108	108
type	test	test	test

	3430905	5032004	4165763	571920
user_action	0.00	1.00	1.0	1.0
sellingprice	94.99	55.99	99.0	64.5
businessunit	0.00	0.00	1.0	2.0
product_gender	0.00	0.00	1.0	1.0
month	11.00	11.00	11.0	11.0
dayOfMonth	6.00	11.00	22.0	17.0
hour	23.00	10.00	2.0	9.0

	Description	Value
0	session_id	3408
1	Target	label
2	Original Data	(5618, 853)
3	Missing Values	False
4	Numeric Features	852
5	Categorical Features	0
6	Ordinal Features	False
7	High Cardinality Features	False
8	High Cardinality Method	None
9	Transformed Train Set	(3932, 652)
10	Transformed Test Set	(1686, 652)
11	Shuffle Train-Test	True
12	Stratify Train-Test	False
13	Fold Generator	KFold
14	Fold Number	10
15	CPU Jobs	-1
16	Use GPU	False
17	Log Experiment	False
18	Experiment Name	reg-default-name
19	USI	ec1e
20	Imputation Type	simple
21	Iterative Imputation Iteration	None
22	Numeric Imputer	mean
23	Iterative Imputation Numeric Model	None
24	Categorical Imputer	constant
25	Iterative Imputation Categorical Model	None
26	Unknown Categoricals Handling	least_frequent
27	Normalize	False
28	Normalize Method	None
29	Transformation	False
30	Transformation Method	None
31	PCA	False
32	PCA Method	None
33	PCA Components	None
34	Ignore Low Variance	False
35	Combine Rare Levels	False
36	Rare Level Threshold	None
37	Numeric Binning	False
38	Remove Outliers	False
39	Outliers Threshold	None
40	Remove Multicollinearity	False
41	Multicollinearity Threshold	None
42	Clustering	False
43	Clustering Iteration	None
44	Polynomial Features	False
45	Polynomial Degree	None
46	Trignometry Features	False
47	Polynomial Threshold	None
48	Group Features	False
49	Feature Selection	False
50	Features Selection Threshold	None
51	Feature Interaction	False
52	Feature Ratio	False
53	Interaction Threshold	None
54	Transform Target	False
55	Transform Target Method	box-cox

user_action	unique_id	favorite	order	search	basket	visit	total_transaction	favorite_percent	visit_percent	search_percent	...	search_perc_times_basket_perc	search_perc_times_order_perc	basket_perc_times_favorite_perc	basket_perc_times_visit_perc	basket_perc_times_search_perc	basket_perc_times_order_perc	order_perc_times_favorite_perc	order_perc_times_visit_perc	order_perc_times_search_perc	order_perc_times_basket_perc
0	9	92.0	22.0	1674.0	122.0	1392.0	3302.0	0.027862	0.421563	0.506965	...	0.018731	0.003378	0.001029	0.015576	0.018731	0.000246	0.000186	0.002809	0.003378	0.000246
1	18	2996.0	1141.0	20321.0	2611.0	44261.0	71330.0	0.042002	0.620510	0.284887	...	0.010428	0.004557	0.001537	0.022713	0.010428	0.000586	0.000672	0.009926	0.004557	0.000586
2	21	1088.0	32.0	2744.0	100.0	4700.0	8664.0	0.125577	0.542475	0.316713	...	0.003656	0.001170	0.001449	0.006261	0.003656	0.000043	0.000464	0.002004	0.001170	0.000043
3	25	948.0	72.0	4068.0	180.0	7812.0	13080.0	0.072477	0.597248	0.311009	...	0.004280	0.001712	0.000997	0.008219	0.004280	0.000076	0.000399	0.003288	0.001712	0.000076
4	31	2043.0	171.0	20583.0	2817.0	48672.0	74286.0	0.027502	0.655197	0.277078	...	0.010507	0.000638	0.001043	0.024846	0.010507	0.000087	0.000063	0.001508	0.000638	0.000087
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2375	7982	0.0	0.0	1.0	0.0	0.0	1.0	0.000000	0.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2376	7990	0.0	0.0	0.0	0.0	1.0	1.0	0.000000	1.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2377	7993	0.0	0.0	0.0	0.0	1.0	1.0	0.000000	1.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2378	7994	0.0	1.0	0.0	0.0	0.0	1.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2379	7998	0.0	0.0	1.0	0.0	0.0	1.0	0.000000	0.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

	unique_id	favorite	order	search	basket	visit	total_transaction	favorite_percent	visit_percent	search_percent	...	visit_Unisex_perc_times_order_Kadın_perc	visit_Unisex_perc_times_order_NotDefined_perc	visit_Unisex_perc_times_order_Unisex_perc	visit_Unisex_perc_times_search_Erkek_perc	visit_Unisex_perc_times_search_Kadın_perc	visit_Unisex_perc_times_search_NotDefined_perc	visit_Unisex_perc_times_search_Unisex_perc	visit_Unisex_perc_times_visit_Erkek_perc	visit_Unisex_perc_times_visit_Kadın_perc	visit_Unisex_perc_times_visit_NotDefined_perc
0	-1.742594	0.718951	1.801352	5.537829	2.736005	2.756309	3.759765	-0.265669	-0.090905	0.505018	...	0.005665	-0.327197	-0.466765	-0.454364	-0.338425	-0.531440	-0.668751	-0.527672	-0.375880	-0.682234
1	-1.738695	33.241858	114.822569	72.736841	67.990151	100.477705	90.986598	-0.175282	0.753711	-0.466645	...	0.539438	-0.215051	-0.296271	-0.364838	0.362932	-0.473108	-0.612210	-0.411934	0.551788	-0.594031
2	-1.737396	11.873502	2.811372	9.393835	2.159231	10.297011	10.635027	0.358949	0.422417	-0.327398	...	0.847485	-0.366883	-0.395350	-0.337066	-0.074023	-0.485527	-0.529711	-0.448209	0.381385	-0.653916
3	-1.735664	10.305593	6.851451	14.165192	4.256592	17.390925	16.297308	0.019521	0.654952	-0.352353	...	0.026829	0.007645	0.248191	-0.347608	0.547873	-0.303729	-0.443758	-0.429230	1.115830	-0.445663
4	-1.733065	22.568879	16.850647	73.681022	73.390856	110.532735	94.776840	-0.267971	0.900973	-0.500814	...	1.236028	0.218597	0.278337	-0.394125	0.825765	0.129474	-0.131974	-0.506578	1.509292	0.139415
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2375	1.710750	-0.311389	-0.420692	-0.491234	-0.462470	-0.416804	-0.472841	-0.443769	-1.880614	2.662200	...	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
2376	1.714215	-0.311389	-0.420692	-0.494838	-0.462470	-0.414525	-0.472841	-0.443769	2.364803	-1.713117	...	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
2377	1.715514	-0.311389	-0.420692	-0.494838	-0.462470	-0.414525	-0.472841	-0.443769	2.364803	-1.713117	...	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
2378	1.715947	-0.311389	-0.319690	-0.494838	-0.462470	-0.416804	-0.472841	-0.443769	-1.880614	-1.713117	...	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046
2379	1.717680	-0.311389	-0.420692	-0.491234	-0.462470	-0.416804	-0.472841	-0.443769	-1.880614	2.662200	...	-0.429890	-0.366883	-0.466765	-0.478150	-0.670794	-0.548456	-0.688463	-0.566615	-0.865457	-0.710046

	unique_id	Label
0	9	0.997439
1	18	0.891450
2	21	0.941691
3	25	0.906425
4	31	0.889785
...	...	...
2375	7982	0.704785
2376	7990	0.588601
2377	7993	0.599433
2378	7994	0.690922
2379	7998	0.594215

	unique_id	favorite	order	search	basket	visit	total_transaction	favorite_percent	visit_percent	search_percent	...	visit_Unisex_perc_times_favorite_Erkek_perc	visit_Unisex_perc_times_favorite_Unisex_perc	visit_Unisex_perc_times_order_NotDefined_perc	visit_Unisex_perc_times_order_Unisex_perc	visit_Unisex_perc_times_search_Erkek_perc	visit_Unisex_perc_times_visit_Erkek_perc	visit_Unisex_perc_times_visit_NotDefined_perc	label	Label	is_true_predicted
1	1.456936	-0.208238	-0.224820	-0.288843	-0.247854	-0.215804	-0.253015	-0.442712	1.205675	-1.315409	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	2.230629	-0.709853	1	0.449955	False
12	-0.435408	-0.208238	-0.224820	-0.290532	-0.270794	-0.212261	-0.252485	-0.442712	2.362849	-1.712936	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.420803	False
23	-0.368273	-0.208238	-0.224820	-0.224660	-0.184770	-0.190117	-0.214631	-0.442712	-0.000004	0.329448	...	-0.276772	-0.455006	-0.366570	-0.466395	1.016615	2.166479	-0.395027	1	0.327214	False
26	1.618060	-0.208238	-0.224820	-0.290532	-0.270794	-0.221562	-0.258044	-0.442712	2.362849	-1.712936	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	0	0.602863	False
31	1.688227	-0.208238	-0.224820	-0.290532	-0.270794	-0.222447	-0.258574	-0.442712	2.362849	-1.712936	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	0	0.601152	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1659	-0.047757	-0.208238	-0.224820	-0.228038	-0.259324	-0.210047	-0.231043	-0.442712	-0.708256	1.368844	...	-0.276772	-0.455006	-0.366570	-0.466395	0.824077	0.072272	-0.709853	0	0.589039	False
1662	0.491056	-0.178738	-0.189389	-0.204392	-0.161831	-0.158672	-0.185247	-0.212653	0.332937	-0.108529	...	1.791306	-0.335267	0.682156	0.033652	0.436064	1.410627	-0.224344	1	0.124623	False
1667	0.098208	-0.205288	-0.224820	-0.267730	-0.270794	-0.190560	-0.232102	-0.379389	1.186579	-0.543971	...	-0.276772	-0.455006	-0.366570	-0.466395	0.183786	-0.284100	-0.511206	0	0.705583	False
1672	1.006914	-0.208238	-0.224820	-0.269419	-0.253589	-0.220233	-0.249838	-0.442712	-1.131364	1.502355	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.302009	False
1685	-0.017871	-0.202338	-0.224820	-0.192569	-0.259324	-0.202961	-0.215160	-0.365189	-0.722950	1.361272	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.243473	0.052512	-0.657577	0	0.770904	False

	unique_id	favorite	order	search	basket	visit	total_transaction	favorite_percent	visit_percent	search_percent	...	visit_Unisex_perc_times_favorite_Erkek_perc	visit_Unisex_perc_times_favorite_Unisex_perc	visit_Unisex_perc_times_order_NotDefined_perc	visit_Unisex_perc_times_order_Unisex_perc	visit_Unisex_perc_times_search_Erkek_perc	visit_Unisex_perc_times_visit_Erkek_perc	visit_Unisex_perc_times_visit_NotDefined_perc	label	Label	is_true_predicted
0	1.075348	-0.208238	-0.207105	-0.281242	-0.270794	-0.222890	-0.255662	-0.442712	-1.880124	2.295460	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.552583	True
1	1.456936	-0.208238	-0.224820	-0.288843	-0.247854	-0.215804	-0.253015	-0.442712	1.205675	-1.315409	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	2.230629	-0.709853	1	0.449955	False
2	-0.502976	-0.208238	-0.207105	-0.265196	-0.247854	-0.200746	-0.236337	-0.442712	0.615742	-0.169596	...	-0.276772	-0.455006	0.534558	-0.466395	-0.477593	-0.566255	-0.709853	1	0.941868	True
3	1.563052	-0.208238	-0.207105	-0.289687	-0.270794	-0.220233	-0.256721	-0.442712	1.302106	-1.166336	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.587690	True
4	1.387635	-0.208238	-0.118528	-0.282931	-0.270794	-0.222890	-0.254868	-0.442712	-1.880124	0.910741	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.507667	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1678	0.245905	-0.208238	-0.224820	-0.275331	-0.253589	-0.218904	-0.250897	-0.442712	-0.607232	0.910741	...	-0.276772	-0.455006	-0.366570	-0.466395	1.246718	0.759675	-0.709853	1	0.772181	True
1679	1.018608	-0.208238	-0.207105	-0.285465	-0.265059	-0.220676	-0.255397	-0.442712	-0.248212	0.305277	...	-0.276772	-0.455006	-0.366570	1.681952	-0.477593	3.729759	-0.709853	0	0.162986	True
1681	1.609397	-0.208238	-0.224820	-0.288843	-0.270794	-0.222890	-0.258309	-0.442712	-1.880124	2.659859	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	1	0.675937	True
1682	1.200523	-0.208238	-0.224820	-0.289687	-0.265059	-0.216690	-0.254603	-0.442712	1.832477	-1.439636	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.477593	-0.566255	-0.709853	0	0.465934	True
1685	-0.017871	-0.202338	-0.224820	-0.192569	-0.259324	-0.202961	-0.215160	-0.365189	-0.722950	1.361272	...	-0.276772	-0.455006	-0.366570	-0.466395	-0.243473	0.052512	-0.657577	0	0.770904	False

	Level1_Category_Name	Level2_Category_Name	Level3_Category_Name	product_gender	contentid	product_name	brand_name	businessunit	basket	favorite	...	1_businessunit_Level1_Level2_Level3	1_businessunit_Level1_Level2	1_businessunit_Level1	1_businessunit	1_Level1_Level2_Level3	1_Level1_Level2	1_Level1	count_Level1_Level2_Level3	count_Level1_Level2	count_Level1
0	Aksesuar	Altın	Gram Altın	Kadın	4422275.0	24 Ayar - 0.5g Altın	Ahlatcı	Mücevher	2.0	2.0	...	0.589286	0.504178	0.35917	0.35917	0.589286	0.504155	0.253730	45	230	28607
1	Aksesuar	Altın	Gram Altın	Kadın	4422282.0	24 Ayar - 1g Altın	Ahlatcı	Mücevher	3.0	2.0	...	0.589286	0.504178	0.35917	0.35917	0.589286	0.504155	0.253730	45	230	28607
2	Aksesuar	Altın	Gram Altın	Kadın	6040895.0	1 Adet 1 Gram 22 Ayar Gram Altın G000021	Tuğrul Kuyumculuk	Mücevher	4.0	3.0	...	0.589286	0.504178	0.35917	0.35917	0.589286	0.504155	0.253730	45	230	28607
3	Aksesuar	Altın	Gram Altın	Kadın	6040912.0	2 Adet 1 Gram 24 Ayar Gram Altın G000042	Tuğrul Kuyumculuk	Mücevher	2.0	0.0	...	0.589286	0.504178	0.35917	0.35917	0.589286	0.504155	0.253730	45	230	28607
4	Aksesuar	Altın	Gram Altın	Kadın	6040933.0	2,5 Gram Külçe Gram Altın IAR 24 Ayar K000001	Tuğrul Kuyumculuk	Mücevher	2.0	0.0	...	0.589286	0.504178	0.35917	0.35917	0.589286	0.504155	0.253730	45	230	28607
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
15602	Yaşam	Otomobil & Motosiklet	Otomobil	Unisex	45695695.0	Deprem Kiti	Clingston	Oto Aksesuar	5.0	2.0	...	0.888889	0.894755	0.89146	0.89063	0.887279	0.889606	0.589207	1389	1681	18135
15603	Yaşam	Otomobil & Motosiklet	Otomobil	Unisex	47141112.0	205/55r16 91v Premiumcontact 6	Continental	Oto Aksesuar	4.0	1.0	...	0.888889	0.894755	0.89146	0.89063	0.887279	0.889606	0.589207	1389	1681	18135
15604	Yaşam	Otomobil & Motosiklet	Otomobil	Unisex	47558441.0	Oil Pro-s Plus Onarıcı Heavy Duty 150.000 Km+ ...	snc	Oto Aksesuar	3.0	1.0	...	0.888889	0.894755	0.89146	0.89063	0.887279	0.889606	0.589207	1389	1681	18135
15605	Yaşam	Otomobil & Motosiklet	Otomobil	Unisex	50364270.0	185/60 R15h 88h Xl G Fıt 4 S Lh71 Bınek 4 Mevs...	Laufenn	Oto Aksesuar	2.0	0.0	...	0.888889	0.894755	0.89146	0.89063	0.887279	0.889606	0.589207	1389	1681	18135
15606	Yaşam	Otomobil & Motosiklet	Otomobil	Unisex	52958586.0	225/45 R17 91w Effıgrıp Perf 2 Fp Bınek Yaz La...	Goodyear	Oto Aksesuar	4.0	0.0	...	0.888889	0.894755	0.89146	0.89063	0.887279	0.889606	0.589207	1389	1681	18135

	Erkek_no_promo	Erkek_promo	Kadın_no_promo	Kadın_promo	NotDefined_no_promo	NotDefined_promo	Unisex_no_promo	Unisex_promo	Erkek_no_promo_percent	Erkek_promo_percent	Kadın_no_promo_percent	Kadın_promo_percent	NotDefined_no_promo_percent	NotDefined_promo_percent	Unisex_no_promo_percent	Unisex_promo_percent
unique_id
1	48.0	2.0	430.0	202.0	490.0	140.0	1414.0	514.0	0.960000	0.040000	0.680380	0.319620	0.777778	0.222222	0.733402	0.266598
2	40.0	0.0	1900.0	1010.0	398.0	138.0	1102.0	648.0	1.000000	0.000000	0.652921	0.347079	0.742537	0.257463	0.629714	0.370286
3	0.0	0.0	1468.0	0.0	48.0	0.0	60.0	0.0	NaN	NaN	1.000000	0.000000	1.000000	0.000000	1.000000	0.000000
4	966.0	18.0	21168.0	2910.0	2406.0	150.0	4950.0	372.0	0.981707	0.018293	0.879143	0.120857	0.941315	0.058685	0.930101	0.069899
5	85.0	0.0	11520.0	3240.0	285.0	255.0	3420.0	1770.0	1.000000	0.000000	0.780488	0.219512	0.527778	0.472222	0.658960	0.341040
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7991	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	0.000000
7992	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	0.000000
7995	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	NaN	NaN	NaN	NaN	0.000000	1.000000	NaN	NaN
7996	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	0.000000
7997	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	1.000000	0.000000	NaN	NaN

	time_stamp	contentid	user_action	sellingprice	product_name	brand_id	brand_name	businessunit	product_gender	category_id	...	Level3_Category_Id	Level3_Category_Name	gender	unique_id	type	month	dayOfMonth	dayofWeek	hour	date
5477152	2020-11-26 10:54:36+00:00	48168289.0	search	92.0	230x250 Balkon Perdesi Balkon Brandası Koyu Kr...	988794.0	ALTINPAMUK	Bahçe & Yapı Market & Hırdavat	Unisex	1989.0	...	2905.0	Motosiklet	F	4618	train	11	26	Thursday	10	2020-11-26
5478353	2020-11-26 10:54:36+00:00	48168289.0	search	92.0	230x250 Balkon Perdesi Balkon Brandası Koyu Kr...	988794.0	ALTINPAMUK	Bahçe & Yapı Market & Hırdavat	Unisex	1989.0	...	2905.0	Motosiklet	F	4618	train	11	26	Thursday	10	2020-11-26

	Level1_Category_Name	Level2_Category_Name	Level3_Category_Name	businessunit	product_gender	gender	time_stamp
1	Aksesuar	Aksesuar Set	Aksesuar Set	Diğer Aksesuarlar	NotDefined	M	0.888889
2	Aksesuar	Aksesuar Set	Aksesuar Set	Ev Dekorasyon	NotDefined	M	1.000000
4	Aksesuar	Aksesuar Set	Aksesuar Set	Mücevher	NotDefined	M	1.000000
5	Aksesuar	Aksesuar Set	Aksesuar Set	Saat B	NotDefined	M	1.000000
6	Aksesuar	Aksesuar Set	Aksesuar Set	Takı	NotDefined	M	1.000000
...	...	...	...	...	...	...	...
8356	Yaşam	Otomobil & Motosiklet	Otomobil	Outdoor Equipment	Unisex	M	0.714286
8357	Yaşam	Otomobil & Motosiklet	Otomobil	Outdoor Equipment	NotDefined	M	1.000000
8360	Yaşam	Otomobil & Motosiklet	Otomobil	TV & Görüntü Ses Sistemleri	Unisex	M	1.000000
8361	Yaşam	Otomobil & Motosiklet	Otomobil	TV & Görüntü Ses Sistemleri	NotDefined	M	1.000000
8363	NotDefined	NotDefined	NotDefined	NotDefined	NotDefined	M	0.645089